429 lines
108 KiB
Plaintext
429 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": "2026-01-25T14:31:20.133709Z",
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"start_time": "2026-01-25T14:31:20.098376Z"
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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": "2026-01-25T14:31:21.085348Z",
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"start_time": "2026-01-25T14:31:20.137715Z"
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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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"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": "2026-01-25T14:31:21.107923Z",
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"start_time": "2026-01-25T14:31:21.085348Z"
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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": "2026-01-25T14:33:58.111630Z",
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"start_time": "2026-01-25T14:31:21.113322Z"
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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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"# --- 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 futures_trading_strategies.SA.ITrend.ITrendStrategy import ITrendStrategy\n",
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"from src.strategies.utils import run_single_backtest\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_SA/KQ_m@CZCE_SA_min15.csv\"\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': 'SA',\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 = \"length\"\n",
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"param1_values = generate_parameter_range(start=10, end=60, step=5)\n",
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"# param1_name = \"exit_threshold\"\n",
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"# param1_values = generate_parameter_range(start=0.1, end=4.1, step=0.4)\n",
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"param2_name = \"range_fraction\"\n",
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"param2_values = generate_parameter_range(start=1, end=31, step=3)\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': ITrendStrategy,\n",
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" # 'hawkes_entry_percent': 0.9,\n",
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" 'strategy_params': {\n",
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" # 'fisher_period': 81\n",
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" }\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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" **result_entry,\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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" **result_entry,\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 121 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": "2026-01-25T14:33:58.341336Z",
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"start_time": "2026-01-25T14:33:58.123304Z"
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}
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},
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"cell_type": "code",
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"source": [
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"optimization_metric = 'total_return'\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'] > 300)\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",
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" grid_analyzer.plot_heatmap() # 这会绘制热力图\n",
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"else:\n",
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|
|
" 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 38 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 length 121 non-null int64 \n",
|
|||
|
|
" 3 range_fraction 121 non-null int64 \n",
|
|||
|
|
" 4 enable_log 121 non-null bool \n",
|
|||
|
|
" 5 初始资金 121 non-null float64\n",
|
|||
|
|
" 6 最终资金 121 non-null float64\n",
|
|||
|
|
" 7 总收益率 121 non-null float64\n",
|
|||
|
|
" 8 年化收益率 121 non-null float64\n",
|
|||
|
|
" 9 最大回撤 121 non-null float64\n",
|
|||
|
|
" 10 夏普比率 121 non-null float64\n",
|
|||
|
|
" 11 卡玛比率 121 non-null float64\n",
|
|||
|
|
" 12 总交易次数 121 non-null int64 \n",
|
|||
|
|
" 13 交易成本 121 non-null float64\n",
|
|||
|
|
" 14 总实现盈亏 121 non-null float64\n",
|
|||
|
|
" 15 胜率 121 non-null float64\n",
|
|||
|
|
" 16 盈亏比 121 non-null float64\n",
|
|||
|
|
" 17 盈利交易次数 121 non-null int64 \n",
|
|||
|
|
" 18 亏损交易次数 121 non-null int64 \n",
|
|||
|
|
" 19 平均每次盈利 121 non-null float64\n",
|
|||
|
|
" 20 平均每次亏损 121 non-null float64\n",
|
|||
|
|
" 21 initial_capital 121 non-null float64\n",
|
|||
|
|
" 22 final_capital 121 non-null float64\n",
|
|||
|
|
" 23 total_return 121 non-null float64\n",
|
|||
|
|
" 24 annualized_return 121 non-null float64\n",
|
|||
|
|
" 25 max_drawdown 121 non-null float64\n",
|
|||
|
|
" 26 sharpe_ratio 121 non-null float64\n",
|
|||
|
|
" 27 calmar_ratio 121 non-null float64\n",
|
|||
|
|
" 28 sortino_ratio 121 non-null float64\n",
|
|||
|
|
" 29 total_trades 121 non-null int64 \n",
|
|||
|
|
" 30 transaction_costs 121 non-null float64\n",
|
|||
|
|
" 31 total_realized_pnl 121 non-null float64\n",
|
|||
|
|
" 32 win_rate 121 non-null float64\n",
|
|||
|
|
" 33 profit_loss_ratio 121 non-null float64\n",
|
|||
|
|
" 34 winning_trades_count 121 non-null int64 \n",
|
|||
|
|
" 35 losing_trades_count 121 non-null int64 \n",
|
|||
|
|
" 36 avg_profit_per_trade 121 non-null float64\n",
|
|||
|
|
" 37 avg_loss_per_trade 121 non-null float64\n",
|
|||
|
|
"dtypes: bool(1), float64(27), int64(9), object(1)\n",
|
|||
|
|
"memory usage: 35.2+ KB\n",
|
|||
|
|
"None\n",
|
|||
|
|
"\n",
|
|||
|
|
"--- 最优参数组合 (按total_return) ---\n",
|
|||
|
|
"main_symbol SA\n",
|
|||
|
|
"trade_volume 1\n",
|
|||
|
|
"length 40\n",
|
|||
|
|
"range_fraction 4\n",
|
|||
|
|
"enable_log False\n",
|
|||
|
|
"初始资金 100000.0\n",
|
|||
|
|
"最终资金 100864.0\n",
|
|||
|
|
"总收益率 0.00864\n",
|
|||
|
|
"年化收益率 0.002472\n",
|
|||
|
|
"最大回撤 0.018438\n",
|
|||
|
|
"夏普比率 0.100988\n",
|
|||
|
|
"卡玛比率 0.13408\n",
|
|||
|
|
"总交易次数 1688\n",
|
|||
|
|
"交易成本 0.0\n",
|
|||
|
|
"总实现盈亏 863.0\n",
|
|||
|
|
"胜率 0.332925\n",
|
|||
|
|
"盈亏比 2.179513\n",
|
|||
|
|
"盈利交易次数 272\n",
|
|||
|
|
"亏损交易次数 545\n",
|
|||
|
|
"平均每次盈利 39.327206\n",
|
|||
|
|
"平均每次亏损 -18.044037\n",
|
|||
|
|
"initial_capital 100000.0\n",
|
|||
|
|
"final_capital 100864.0\n",
|
|||
|
|
"total_return 0.00864\n",
|
|||
|
|
"annualized_return 0.002472\n",
|
|||
|
|
"max_drawdown 0.018438\n",
|
|||
|
|
"sharpe_ratio 0.100988\n",
|
|||
|
|
"calmar_ratio 0.13408\n",
|
|||
|
|
"sortino_ratio 0.140057\n",
|
|||
|
|
"total_trades 1688\n",
|
|||
|
|
"transaction_costs 0.0\n",
|
|||
|
|
"total_realized_pnl 863.0\n",
|
|||
|
|
"win_rate 0.332925\n",
|
|||
|
|
"profit_loss_ratio 2.179513\n",
|
|||
|
|
"winning_trades_count 272\n",
|
|||
|
|
"losing_trades_count 545\n",
|
|||
|
|
"avg_profit_per_trade 39.327206\n",
|
|||
|
|
"avg_loss_per_trade -18.044037\n",
|
|||
|
|
"Name: 67, dtype: object\n",
|
|||
|
|
"\n",
|
|||
|
|
"--- 动态网格搜索完成 ---\n",
|
|||
|
|
"\n",
|
|||
|
|
"--- 最佳参数组合 ---\n",
|
|||
|
|
" length: 40\n",
|
|||
|
|
" range_fraction: 4\n",
|
|||
|
|
" total_return: 0.0086\n",
|
|||
|
|
"[10, 60, 1, 31]\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
|
|||
|
|
}
|