368 lines
22 KiB
Plaintext
368 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "782ec73f",
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"metadata": {},
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"outputs": [],
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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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"from src.strategies.SimpleLimitBuyStrategy import SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategy\n",
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"\n",
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"\n",
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"import builtins\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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"origin_print = print\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "76f9a2e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# --- 单个回测任务函数 ---\n",
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"# 这个函数将在每个独立的进程中运行，因此它必须是自包含的\n",
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"def run_single_backtest(\n",
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" combination: Tuple[float, float], # 传入当前参数组合\n",
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" common_config: Dict[str, Any] # 传入公共配置 (如数据路径, 初始资金等)\n",
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") -> Optional[Dict[str, Any]]:\n",
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" \"\"\"\n",
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" 运行单个参数组合的回测任务。\n",
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" 此函数将在一个独立的进程中执行。\n",
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" \"\"\"\n",
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" p1_value, p2_value = combination\n",
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"\n",
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" # 从 common_config 中获取必要的配置\n",
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" symbol = common_config['symbol']\n",
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" data_path = common_config['data_path']\n",
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" initial_capital = common_config['initial_capital']\n",
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" slippage_rate = common_config['slippage_rate']\n",
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" commission_rate = common_config['commission_rate']\n",
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" start_time = common_config['start_time']\n",
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" end_time = common_config['end_time']\n",
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" roll_over_mode = common_config['roll_over_mode']\n",
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" # bar_duration_seconds = common_config['bar_duration_seconds'] # 如果DataManager需要，可以再传\n",
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" param1_name = common_config['param1_name']\n",
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" param2_name = common_config['param2_name']\n",
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" optimization_metric = common_config['optimization_metric']\n",
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"\n",
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" # 每个进程内部独立初始化 DataManager 和 BacktestEngine\n",
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" # 确保每个进程有自己的数据副本和模拟状态\n",
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" data_manager = DataManager(\n",
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" file_path=data_path,\n",
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" symbol=symbol,\n",
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" # bar_duration_seconds=bar_duration_seconds, # 如果DataManager需要，根据数据文件路径推断或者额外参数传入\n",
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" # start_date=start_time.date(), # DataManager 现在通过 file_path 和 symbol 处理数据\n",
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" # end_date=end_time.date(),\n",
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" )\n",
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" # data_manager.load_data() # DataManager 内部加载数据\n",
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"\n",
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" # 策略参数\n",
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" strategy_parameters = {\n",
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" 'trade_volume': 1,\n",
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" param1_name: p1_value,\n",
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" param2_name: p2_value,\n",
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" 'max_position': 10,\n",
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" 'enable_log': False, # 在网格搜索时通常关闭策略内部的详细日志\n",
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" }\n",
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"\n",
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" # 打印当前进程正在处理的组合信息\n",
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" # 注意：多进程打印会交错显示\n",
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" # print(f\"--- 正在运行组合: {strategy_parameters} (PID: {multiprocessing.current_process().pid}) ---\")\n",
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"\n",
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" try:\n",
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" # 初始化回测引擎\n",
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" engine = BacktestEngine(\n",
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" data_manager=data_manager,\n",
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" strategy_class=common_config['strategy'],\n",
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" strategy_params=strategy_parameters,\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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" roll_over_mode=True, # 保持换月模式\n",
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" start_time=common_config['start_time'],\n",
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" end_time=common_config['end_time']\n",
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" )\n",
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" # 运行回测，传入时间范围\n",
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" engine.run_backtest()\n",
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"\n",
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" # 获取回测结果并分析\n",
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" results = engine.get_backtest_results()\n",
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" portfolio_snapshots = results[\"portfolio_snapshots\"]\n",
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" trade_history = results[\"trade_history\"]\n",
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" bars = results[\"all_bars\"]\n",
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" initial_capital_result = results[\"initial_capital\"]\n",
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"\n",
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" if portfolio_snapshots:\n",
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" analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result)\n",
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"\n",
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" # analyzer.generate_report()\n",
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" # analyzer.plot_performance()\n",
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" metrics = analyzer.calculate_all_metrics()\n",
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"\n",
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" # 将当前组合的参数和性能指标存储起来\n",
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" result_entry = {**strategy_parameters, **metrics}\n",
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" # print(f\" 组合 {combination} 完成。{optimization_metric}: {metrics.get(optimization_metric, 0.0):.4f} (PID: {multiprocessing.current_process().pid})\")\n",
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" return result_entry\n",
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" else:\n",
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" print(f\" 组合 {strategy_parameters} 没有生成投资组合快照，无法进行结果分析。(PID: {multiprocessing.current_process().pid})\")\n",
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" # 返回一个包含参数和默认0值的结果，以便追踪失败组合\n",
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" return {**strategy_parameters, \"total_return\": 0.0, \"annualized_return\": 0.0, \"sharpe_ratio\": 0.0, \"max_drawdown\": 0.0, \"error\": \"No portfolio snapshots\"}\n",
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" except Exception as e:\n",
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" import traceback\n",
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" error_trace = traceback.format_exc()\n",
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" print(f\" 组合 {strategy_parameters} 运行失败: {e}\\n{error_trace} (PID: {multiprocessing.current_process().pid})\")\n",
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" # 返回错误信息，以便后续处理\n",
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" return {**strategy_parameters, \"error\": str(e), \"traceback\": error_trace}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "c0984689",
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"metadata": {},
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"outputs": [],
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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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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "239e9ca0",
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"metadata": {},
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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 1681 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 10 个进程 ---\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 69\u001b[39m\n\u001b[32m 63\u001b[39m args_for_starmap = [\n\u001b[32m 64\u001b[39m (combo, common_config_for_processes) \u001b[38;5;28;01mfor\u001b[39;00m combo \u001b[38;5;129;01min\u001b[39;00m param_combinations\n\u001b[32m 65\u001b[39m ]\n\u001b[32m 67\u001b[39m \u001b[38;5;66;03m# 使用 starmap() 来并行执行 run_single_backtest 函数\u001b[39;00m\n\u001b[32m 68\u001b[39m \u001b[38;5;66;03m# starmap 是阻塞的，会等待所有任务完成并返回结果列表\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, result_entry \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[43mpool\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstarmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_single_backtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs_for_starmap\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[32m 70\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result_entry: \u001b[38;5;66;03m# 确保结果不为空\u001b[39;00m\n\u001b[32m 71\u001b[39m all_results.append(result_entry)\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/multiprocessing/pool.py:375\u001b[39m, in \u001b[36mPool.starmap\u001b[39m\u001b[34m(self, func, iterable, chunksize)\u001b[39m\n\u001b[32m 369\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mstarmap\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, iterable, chunksize=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 370\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m'''\u001b[39;00m\n\u001b[32m 371\u001b[39m \u001b[33;03m Like `map()` method but the elements of the `iterable` are expected to\u001b[39;00m\n\u001b[32m 372\u001b[39m \u001b[33;03m be iterables as well and will be unpacked as arguments. Hence\u001b[39;00m\n\u001b[32m 373\u001b[39m \u001b[33;03m `func` and (a, b) becomes func(a, b).\u001b[39;00m\n\u001b[32m 374\u001b[39m \u001b[33;03m '''\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m375\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_map_async\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstarmapstar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/multiprocessing/pool.py:768\u001b[39m, in \u001b[36mApplyResult.get\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 767\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mget\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m768\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 769\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.ready():\n\u001b[32m 770\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/multiprocessing/pool.py:765\u001b[39m, in \u001b[36mApplyResult.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 764\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwait\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m765\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_event\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/threading.py:655\u001b[39m, in \u001b[36mEvent.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 653\u001b[39m signaled = \u001b[38;5;28mself\u001b[39m._flag\n\u001b[32m 654\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m signaled:\n\u001b[32m--> \u001b[39m\u001b[32m655\u001b[39m signaled = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_cond\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 656\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m signaled\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/threading.py:355\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m355\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 356\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
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"\u001b[31mKeyboardInterrupt\u001b[39m: "
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]
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}
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],
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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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"if __name__ == \"__main__\":\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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" # --- 全局配置 ---\n",
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" data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min60.csv\"\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 = 0.0002\n",
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" global_config = {\n",
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" 'symbol': 'KQ_m@CZCE_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 = \"open_range_factor_1_ago\"\n",
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" param1_values = generate_parameter_range(start=-2, end=2, step=0.1)\n",
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" param2_name = \"open_range_factor_7_ago\"\n",
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" param2_values = generate_parameter_range(start=-2, end=2, step=0.1)\n",
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" optimization_metric = 'sharpe_ratio'\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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" '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(2025, 1, 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",
|
||
" 'param2_name': param2_name,\n",
|
||
" 'optimization_metric': optimization_metric,\n",
|
||
" 'strategy': SimpleLimitBuyStrategyLong\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() / 2)\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",
|
||
" # 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 ---\")\n",
|
||
"\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('-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], 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[(results_df['total_trades'] > 200) & (results_df['total_return'] > 0)]\n",
|
||
" if len(normal_results) > 0:\n",
|
||
" best_result = normal_results.loc[(normal_results[optimization_metric].idxmax())]\n",
|
||
" print(\"\\n--- 最优参数组合 (按夏普比率) ---\")\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。\")"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|