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NewQuant/grid_search_multi_process2.ipynb

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tqsdk实盘
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{
"cells": [
{
"cell_type": "code",
"id": "782ec73f",
"metadata": {
"ExecuteTime": {
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"end_time": "2025-07-30T05:53:47.532034Z",
"start_time": "2025-07-30T05:53:46.955612Z"
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}
},
"source": [
"import pandas as pd\n",
"from datetime import datetime\n",
"import itertools\n",
"from typing import Dict, Any, List, Tuple, Optional\n",
"import multiprocessing # 导入 multiprocessing 模块\n",
"import math # 保留 math 导入，因为您的策略内部可能需要用到数学函数\n",
"\n",
"# 导入所有必要的模块\n",
"# 请确保这些导入路径与您的项目结构相符\n",
"from src.analysis.grid_search_analyzer import GridSearchAnalyzer\n",
"from src.analysis.result_analyzer import ResultAnalyzer\n",
"from src.common_utils import generate_parameter_range\n",
"from src.data_manager import DataManager\n",
"from src.backtest_engine import BacktestEngine\n",
"# 导入策略类\n",
"from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, \\\n",
" SimpleLimitBuyStrategy\n",
"\n",
"import builtins\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"origin_print = print\n"
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],
"outputs": [],
"execution_count": 1
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},
{
"cell_type": "code",
"id": "76f9a2e9",
"metadata": {
"ExecuteTime": {
1、准备更新order单前备份
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"end_time": "2025-07-30T05:53:47.571575Z",
"start_time": "2025-07-30T05:53:47.544992Z"
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}
},
"source": [
"\n",
"# --- 单个回测任务函数 ---\n",
"# 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",
"def run_single_backtest(\n",
" combination: Tuple[float, float], # 传入当前参数组合\n",
" common_config: Dict[str, Any] # 传入公共配置 (如数据路径, 初始资金等)\n",
") -> Optional[Dict[str, Any]]:\n",
" \"\"\"\n",
" 运行单个参数组合的回测任务。\n",
" 此函数将在一个独立的进程中执行。\n",
" \"\"\"\n",
" p1_value, p2_value = combination\n",
"\n",
" # 从 common_config 中获取必要的配置\n",
" symbol = common_config['symbol']\n",
" data_path = common_config['data_path']\n",
" initial_capital = common_config['initial_capital']\n",
" slippage_rate = common_config['slippage_rate']\n",
" commission_rate = common_config['commission_rate']\n",
" start_time = common_config['start_time']\n",
" end_time = common_config['end_time']\n",
" roll_over_mode = common_config['roll_over_mode']\n",
" # bar_duration_seconds = common_config['bar_duration_seconds'] # 如果DataManager需要，可以再传\n",
" param1_name = common_config['param1_name']\n",
" param2_name = common_config['param2_name']\n",
"\n",
" # 每个进程内部独立初始化 DataManager 和 BacktestEngine\n",
" # 确保每个进程有自己的数据副本和模拟状态\n",
" data_manager = DataManager(\n",
" file_path=data_path,\n",
" symbol=symbol,\n",
" # 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",
" # start_date=start_time.date(), # DataManager 现在通过 file_path 和 symbol 处理数据\n",
" # end_date=end_time.date(),\n",
" )\n",
" # data_manager.load_data() # DataManager 内部加载数据\n",
"\n",
" # 策略参数\n",
" strategy_parameters = {\n",
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" 'main_symbol': common_config['main_symbol'],\n",
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" 'trade_volume': 1,\n",
" param1_name: p1_value,\n",
" param2_name: p2_value,\n",
" 'max_position': 20,\n",
" '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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" 'stop_loss_points': common_config['stop_loss_points'],\n",
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" 'lag': common_config['lag']\n",
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" }\n",
"\n",
" # 打印当前进程正在处理的组合信息\n",
" # 注意：多进程打印会交错显示\n",
" # print(f\"--- 正在运行组合: {strategy_parameters} (PID: {multiprocessing.current_process().pid}) ---\")\n",
"\n",
" try:\n",
" # 初始化回测引擎\n",
" engine = BacktestEngine(\n",
" data_manager=data_manager,\n",
" strategy_class=common_config['strategy'],\n",
" strategy_params=strategy_parameters,\n",
" initial_capital=initial_capital,\n",
" slippage_rate=slippage_rate,\n",
" commission_rate=commission_rate,\n",
" roll_over_mode=True, # 保持换月模式\n",
" start_time=common_config['start_time'],\n",
" end_time=common_config['end_time']\n",
" )\n",
" # 运行回测，传入时间范围\n",
" engine.run_backtest()\n",
"\n",
" # 获取回测结果并分析\n",
" results = engine.get_backtest_results()\n",
" portfolio_snapshots = results[\"portfolio_snapshots\"]\n",
" trade_history = results[\"trade_history\"]\n",
" bars = results[\"all_bars\"]\n",
" initial_capital_result = results[\"initial_capital\"]\n",
"\n",
" if portfolio_snapshots:\n",
" analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result)\n",
"\n",
" # analyzer.generate_report()\n",
" # analyzer.plot_performance()\n",
" metrics = analyzer.calculate_all_metrics()\n",
"\n",
" # 将当前组合的参数和性能指标存储起来\n",
" result_entry = {**strategy_parameters, **metrics}\n",
" return result_entry\n",
" else:\n",
" print(\n",
" 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",
" # 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",
" return {**strategy_parameters, \"total_return\": 0.0, \"annualized_return\": 0.0, \"sharpe_ratio\": 0.0,\n",
" \"max_drawdown\": 0.0, \"error\": \"No portfolio snapshots\"}\n",
" except Exception as e:\n",
" import traceback\n",
" error_trace = traceback.format_exc()\n",
" print(\n",
" f\" 组合 {strategy_parameters} 运行失败: {e}\\n{error_trace} (PID: {multiprocessing.current_process().pid})\")\n",
" # 返回错误信息，以便后续处理\n",
" return {**strategy_parameters, \"error\": str(e), \"traceback\": error_trace}\n",
"\n"
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],
"outputs": [],
"execution_count": 2
tqsdk实盘
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},
{
"cell_type": "code",
"id": "c0984689",
"metadata": {
"ExecuteTime": {
1、准备更新order单前备份
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"end_time": "2025-07-30T05:53:47.640401Z",
"start_time": "2025-07-30T05:53:47.618738Z"
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}
},
"source": [
"\n",
"def slient_print(*args):\n",
" pass\n"
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],
"outputs": [],
"execution_count": 3
tqsdk实盘
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},
{
"cell_type": "code",
"id": "8b6d9f4cd97a863d",
"metadata": {
"ExecuteTime": {
1、准备更新order单前备份
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"end_time": "2025-07-30T06:13:11.572515Z",
"start_time": "2025-07-30T05:53:47.681952Z"
tqsdk实盘
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}
},
"source": [
"\n",
"# --- 主执行块 ---\n",
"# 这是多进程代码的入口点，必须在 'if __name__ == \"__main__\":' 保护块中\n",
"# 确保 autoreload 启用 (在Jupyter Notebook中使用，纯Python脚本运行时可移除)\n",
"# %load_ext autoreload\n",
"# %autoreload 2\n",
"\n",
"# --- 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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"data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@SHFE_rb/KQ_m@SHFE_rb_min15.csv\"\n",
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"# data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_SR/KQ_m@CZCE_SR_min60.csv\"\n",
"# data_file_path = '/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min60.csv'\n",
"\n",
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"\n",
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"initial_capital = 100000.0\n",
"slippage_rate = 0.0000\n",
"commission_rate = 0.0001\n",
"global_config = {\n",
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" 'symbol': 'KQ_m@SHFE_rb',\n",
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"}\n",
"# 确保每个合约的tick_size在这里定义或获取\n",
"RB_TICK_SIZE = 1.0 # 螺纹钢的最小变动单位\n",
"\n",
"# --- 定义参数网格 ---\n",
"param1_name = \"range_factor\"\n",
"param1_values = generate_parameter_range(start=0, end=3, step=0.1)\n",
"param2_name = \"profit_factor\"\n",
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"param2_values = generate_parameter_range(start=0, end=5, step=0.1)\n",
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"optimization_metric = 'sharpe_ratio'\n",
"\n",
"# 生成所有参数组合\n",
"param_combinations = list(itertools.product(param1_values, param2_values))\n",
"total_combinations = len(param_combinations)\n",
"print(f\"总计 {total_combinations} 种参数组合需要回测。\")\n",
"\n",
"all_results: List[Dict[str, Any]] = []\n",
"grid_results: List[Dict[str, Any]] = []\n",
"\n",
"# 准备传递给每个子进程的公共配置字典\n",
"common_config_for_processes = {\n",
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" 'main_symbol': global_config['symbol'],\n",
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" 'symbol': global_config['symbol'],\n",
" 'data_path': data_file_path,\n",
" 'initial_capital': initial_capital,\n",
" 'slippage_rate': slippage_rate,\n",
" 'commission_rate': commission_rate,\n",
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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",
" 'roll_over_mode': True, # 保持换月模式\n",
" 'param1_name': param1_name,\n",
" 'param2_name': param2_name,\n",
" 'optimization_metric': optimization_metric,\n",
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" 'strategy': SimpleLimitBuyStrategyLong,\n",
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" 'lag': 1,\n",
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" 'stop_loss_points': 10\n",
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"}\n",
"\n",
"# 确定要使用的进程数量 (通常是CPU核心数)\n",
"num_processes = int(multiprocessing.cpu_count() * 0.75)\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 ---\")"
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],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"总计 1581 种参数组合需要回测。\n",
"--- 启动多进程网格搜索，使用 15 个进程 ---\n",
"\n",
"--- 网格搜索回测完毕 ---\n"
]
}
],
"execution_count": 4
1、新增SMCPureH1LongStrategy策略 2、修复实盘bug
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},
{
"cell_type": "code",
"id": "239e9ca0",
"metadata": {
"ExecuteTime": {
1、准备更新order单前备份
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"end_time": "2025-07-30T06:13:12.412892Z",
"start_time": "2025-07-30T06:13:11.593961Z"
1、新增SMCPureH1LongStrategy策略 2、修复实盘bug
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}
},
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"source": [
"\n",
"# --- 5. 后处理和最佳结果选择 ---\n",
"if all_results:\n",
" results_df = pd.DataFrame(all_results)\n",
" # print(\"\\n--- 所有回测结果汇总 ---\")\n",
" # # 确保打印时浮点数格式化\n",
" # pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
" # print(results_df.to_string())\n",
"\n",
" # 找到最佳组合 (排除有错误的)\n",
" # 过滤掉包含 'error' 键的行，或者 'error' 键的值不为空的行\n",
" # 同时确保优化指标是数值，并且不为无穷大\n",
" print(results_df.info())\n",
" successful_results_df = results_df[(pd.to_numeric(results_df[optimization_metric], errors='coerce').notna()) &\n",
" (pd.to_numeric(results_df[optimization_metric], errors='coerce') != float(\n",
" '-inf'))\n",
" ].copy() # 使用 .copy() 避免 SettingWithCopyWarning\n",
"\n",
" if not successful_results_df.empty and optimization_metric in successful_results_df.columns:\n",
" # 确保优化指标列是数值类型\n",
" successful_results_df[optimization_metric] = pd.to_numeric(successful_results_df[optimization_metric],\n",
" errors='coerce')\n",
"\n",
" if not successful_results_df.empty and optimization_metric in successful_results_df.columns:\n",
" # 过滤掉NaN值，如果所有夏普比率都是NaN，则可能没有有效结果\n",
" normal_results = successful_results_df[\n",
" (results_df['total_trades'] > 0)\n",
" # &\n",
" # (results_df['profit_factor'] < 3.)\n",
" ]\n",
" if len(normal_results) > 0:\n",
" best_result = normal_results.loc[(normal_results[optimization_metric].idxmax())]\n",
" print(f\"\\n--- 最优参数组合 (按{optimization_metric}) ---\")\n",
" print(best_result)\n",
" else:\n",
" print('ERROR!!!!!!!!!!!!!!!!!!!!')\n",
"\n",
" # 找到最大值的索引\n",
" # best_result = successful_results_df.loc[successful_results_df[optimization_metric].idxmax()]\n",
" # print(f\"\\n--- 最优参数组合 (按 {optimization_metric}) ---\")\n",
" # print(best_result)\n",
"\n",
" # 导出到CSV\n",
" output_filename = f\"grid_search_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv\"\n",
" # results_df.to_csv(output_filename, index=False, encoding='utf-8')\n",
" # print(f\"\\n所有结果已导出到: {output_filename}\")\n",
"\n",
" # 打印枢轴表\n",
" grid_df = pd.DataFrame(grid_results)\n",
" # 确保优化指标列是数值类型，非数值的（如 -inf）在pandas中可能被正确处理\n",
" grid_df[optimization_metric] = pd.to_numeric(grid_df[optimization_metric], errors='coerce')\n",
"\n",
" pivot_table = grid_df.pivot_table(\n",
" index=param1_name, columns=param2_name, values=optimization_metric\n",
" )\n",
" # print(f\"\\n{optimization_metric} 网格结果 (Pivoted):\")\n",
" # print(pivot_table.to_string())\n",
" else:\n",
" print(f\"\\n没有成功的组合结果可供分析，或优化指标 '{optimization_metric}' 不在结果中，或所有组合均失败。\")\n",
"else:\n",
" print(\"没有可用的回测结果。\")\n",
"print(\"\\n--- 动态网格搜索完成 ---\")\n",
"\n",
"# --- 6. 可视化 (依赖 GridSearchAnalyzer) ---\n",
"if grid_results:\n",
" grid_analyzer = GridSearchAnalyzer(grid_results, optimization_metric)\n",
" grid_analyzer.find_best_parameters() # 这会找到并打印最佳参数\n",
" grid_analyzer.plot_heatmap() # 这会绘制热力图\n",
"else:\n",
" print(\"\\n没有生成任何网格搜索结果，无法进行分析。\")"
],
tqsdk实盘
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
1、新增SMCPureH1LongStrategy策略 2、修复实盘bug
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"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1581 entries, 0 to 1580\n",
"Data columns (total 40 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 main_symbol 1581 non-null object \n",
" 1 trade_volume 1581 non-null int64 \n",
" 2 range_factor 1581 non-null float64\n",
" 3 profit_factor 1581 non-null float64\n",
" 4 max_position 1581 non-null int64 \n",
" 5 enable_log 1581 non-null bool \n",
" 6 stop_loss_points 1581 non-null int64 \n",
" 7 lag 1581 non-null int64 \n",
" 8 初始资金 1581 non-null float64\n",
" 9 最终资金 1581 non-null float64\n",
" 10 总收益率 1581 non-null float64\n",
" 11 年化收益率 1581 non-null float64\n",
" 12 最大回撤 1581 non-null float64\n",
" 13 夏普比率 1581 non-null float64\n",
" 14 卡玛比率 1581 non-null float64\n",
" 15 总交易次数 1581 non-null int64 \n",
" 16 交易成本 1581 non-null float64\n",
" 17 总实现盈亏 1581 non-null float64\n",
" 18 胜率 1581 non-null float64\n",
" 19 盈亏比 1581 non-null float64\n",
" 20 盈利交易次数 1581 non-null int64 \n",
" 21 亏损交易次数 1581 non-null int64 \n",
" 22 平均每次盈利 1581 non-null float64\n",
" 23 平均每次亏损 1581 non-null float64\n",
" 24 initial_capital 1581 non-null float64\n",
" 25 final_capital 1581 non-null float64\n",
" 26 total_return 1581 non-null float64\n",
" 27 annualized_return 1581 non-null float64\n",
" 28 max_drawdown 1581 non-null float64\n",
" 29 sharpe_ratio 1581 non-null float64\n",
" 30 calmar_ratio 1581 non-null float64\n",
" 31 total_trades 1581 non-null int64 \n",
" 32 transaction_costs 1581 non-null float64\n",
" 33 total_realized_pnl 1581 non-null float64\n",
" 34 win_rate 1581 non-null float64\n",
" 35 profit_loss_ratio 1581 non-null float64\n",
" 36 winning_trades_count 1581 non-null int64 \n",
" 37 losing_trades_count 1581 non-null int64 \n",
" 38 avg_profit_per_trade 1581 non-null float64\n",
" 39 avg_loss_per_trade 1581 non-null float64\n",
"dtypes: bool(1), float64(28), int64(10), object(1)\n",
"memory usage: 483.4+ KB\n",
"None\n",
tqsdk实盘
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"\n",
1、新增SMCPureH1LongStrategy策略 2、修复实盘bug
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"--- 最优参数组合 (按sharpe_ratio) ---\n",
1、准备更新order单前备份
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"main_symbol KQ_m@SHFE_rb\n",
"trade_volume 1\n",
"range_factor 2.3\n",
"profit_factor 4.9\n",
"max_position 20\n",
"enable_log False\n",
"stop_loss_points 10\n",
"lag 1\n",
"初始资金 100000.0\n",
"最终资金 99834.9266\n",
"总收益率 -0.001651\n",
"年化收益率 -0.000474\n",
"最大回撤 0.013207\n",
"夏普比率 -0.024377\n",
"卡玛比率 -0.035895\n",
"总交易次数 348\n",
"交易成本 141.0734\n",
"总实现盈亏 -12.0\n",
"胜率 0.241379\n",
"盈亏比 3.127717\n",
"盈利交易次数 42\n",
"亏损交易次数 132\n",
"平均每次盈利 59.02381\n",
"平均每次亏损 -18.871212\n",
"initial_capital 100000.0\n",
"final_capital 99834.9266\n",
"total_return -0.001651\n",
"annualized_return -0.000474\n",
"max_drawdown 0.013207\n",
"sharpe_ratio -0.024377\n",
"calmar_ratio -0.035895\n",
"total_trades 348\n",
"transaction_costs 141.0734\n",
"total_realized_pnl -12.0\n",
"win_rate 0.241379\n",
"profit_loss_ratio 3.127717\n",
"winning_trades_count 42\n",
"losing_trades_count 132\n",
"avg_profit_per_trade 59.02381\n",
"avg_loss_per_trade -18.871212\n",
"Name: 1222, dtype: object\n",
1、新增SMCPureH1LongStrategy策略 2、修复实盘bug
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"\n",
"--- 动态网格搜索完成 ---\n",
"\n",
"--- 最佳参数组合 ---\n",
1、准备更新order单前备份
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" range_factor: 2.3\n",
" profit_factor: 4.9\n",
" sharpe_ratio: -0.0244\n",
1、新增SMCPureH1LongStrategy策略 2、修复实盘bug
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"[0, 3.0, 0, 5.0]\n"
tqsdk实盘
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]
1、新增SMCPureH1LongStrategy策略 2、修复实盘bug
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},
{
"data": {
"text/plain": [
"<Figure size 1000x800 with 2 Axes>"
1、准备更新order单前备份
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],
"image/png": "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
1、新增SMCPureH1LongStrategy策略 2、修复实盘bug
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},
"metadata": {},
"output_type": "display_data"
tqsdk实盘
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}
],
1、准备更新order单前备份
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"execution_count": 5
tqsdk实盘
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}
],
"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
}
Reference in New Issue Copy Permalink