415 lines
113 KiB
Plaintext
415 lines
113 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": "2025-11-05T13:06:55.526625Z",
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"start_time": "2025-11-05T13:06:55.505124Z"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[autoreload of src.strategies.AreaReversal.AreaReversalStrategy failed: Traceback (most recent call last):\n",
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" File \"D:\\Python\\conda\\envs\\quant\\Lib\\site-packages\\IPython\\extensions\\autoreload.py\", line 322, in check\n",
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" elif self.deduper_reloader.maybe_reload_module(m):\n",
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" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
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" File \"D:\\Python\\conda\\envs\\quant\\Lib\\site-packages\\IPython\\extensions\\deduperreload\\deduperreload.py\", line 545, in maybe_reload_module\n",
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" new_source_code = f.read()\n",
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" ^^^^^^^^\n",
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"UnicodeDecodeError: 'gbk' codec can't decode byte 0x89 in position 364: illegal multibyte sequence\n",
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"]\n"
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]
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}
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],
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"execution_count": 6
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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": "2025-11-05T13:06:55.549940Z",
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"start_time": "2025-11-05T13:06:55.531734Z"
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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": 7
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-05T13:06:55.568284Z",
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"start_time": "2025-11-05T13:06:55.553269Z"
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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": 8
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},
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{
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"metadata": {
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"jupyter": {
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"is_executing": true
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},
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"ExecuteTime": {
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"start_time": "2025-11-05T13:06:55.572303Z"
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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 src.strategies.AreaReversal.AreaReversalStrategy import AreaReversalStrategy\n",
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"from src.strategies.AreaReversal.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_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 = \"ma_period\"\n",
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"param1_values = generate_parameter_range(start=5, end=25, step=2)\n",
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"param2_name = \"area_window\"\n",
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"param2_values = generate_parameter_range(start=5, end=25, step=2)\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': AreaReversalStrategy,\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": "3428ed77effae112",
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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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]
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}
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],
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"execution_count": null
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-05T09:00:43.930695Z",
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"start_time": "2025-11-05T09:00:43.637014Z"
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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",
|
|||
|
|
" 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 ma_period 121 non-null int64 \n",
|
|||
|
|
" 3 area_window 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",
|
|||
|
|
"ERROR!!!!!!!!!!!!!!!!!!!!\n",
|
|||
|
|
"\n",
|
|||
|
|
"--- 动态网格搜索完成 ---\n",
|
|||
|
|
"\n",
|
|||
|
|
"--- 最佳参数组合 ---\n",
|
|||
|
|
" ma_period: 5\n",
|
|||
|
|
" area_window: 11\n",
|
|||
|
|
" total_return: 0.0090\n",
|
|||
|
|
"[5, 25, 5, 25]\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
|
|||
|
|
}
|