{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "initial_id", "metadata": { "ExecuteTime": { "end_time": "2025-07-22T07:44:51.375234Z", "start_time": "2025-07-22T07:44:51.352161Z" }, "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime\n", "%load_ext autoreload\n", "%autoreload 2\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "a559dfcf", "metadata": { "ExecuteTime": { "end_time": "2025-07-22T07:44:56.927700Z", "start_time": "2025-07-22T07:44:51.391111Z" } }, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'src'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mturtle\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m down\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01manalysis\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mresult_analyzer\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ResultAnalyzer\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# 导入所有必要的模块\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata_manager\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataManager\n", "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'src'" ] } ], "source": [ "\n", "from turtle import down\n", "from src.analysis.result_analyzer import ResultAnalyzer\n", "# 导入所有必要的模块\n", "from src.data_manager import DataManager\n", "from src.backtest_engine import BacktestEngine\n", "from src.indicators.indicator_list import INDICATOR_LIST\n", "from src.indicators.indicators import RSI, BollingerBandwidth, HistoricalRange, NormalizedATR, RateOfChange, StochasticOscillator\n", "from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategy\n", "\n", "\n", "# --- 配置参数 ---\n", "# 获取当前脚本所在目录，假设数据文件在项目根目录下的 data 文件夹内\n", "# data_file_path = '/mnt/d/PyProject/NewQuant/data/data/SHFE_rb2510/SHFE_rb2510_min60.csv'\n", "# data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min60.csv\"\n", "# data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@SHFE_rb/KQ_m@SHFE_rb_min60.csv\"\n", "data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min60.csv\"\n", "\n", "initial_capital = 100000.0\n", "slippage_rate = 0.000 # 假设每笔交易0.1%的滑点\n", "commission_rate = 0.0001 # 假设每笔交易0.02%的佣金\n", "\n", "global_config = {\n", " 'symbol': 'KQ_m@CZCE_MA',\n", "}\n", "\n", "# Short 可用\n", "strategy_parameters = {\n", " 'main_symbol': \"MA\", # 根据您的数据文件中的品种名称调整\n", " 'trade_volume': 1,\n", " 'lag': 7,\n", " # 'range_factor': 1.8, # 示例值，需要通过网格搜索优化\n", " # 'profit_factor': 2.8, # 示例值\n", " # 'range_factor': 1.6, # 示例值，需要通过网格搜索优化\n", " # 'profit_factor': 2.1, # 示例值\n", " 'range_factor_l': 1.8, # 示例值，需要通过网格搜索优化\n", " 'profit_factor_l': 2.8, # 示例值\n", " 'range_factor_s': 1.6, # 示例值，需要通过网格搜索优化\n", " 'profit_factor_s': 2.1, # 示例值\n", " 'max_position': 10,\n", " 'enable_log': True,\n", " 'stop_loss_points': 20,\n", " 'use_indicator': True,\n", " # 'indicator': HistoricalRange(11, 25, 20),\n", " # 'indicator': BollingerBandwidth(window=20, nbdev=2.0, down_bound=1.9, up_bound=3.25),\n", " 'indicator_l': HistoricalRange(11, 25, 20),\n", " 'indicator_s': BollingerBandwidth(window=20, nbdev=2.0, down_bound=1.9, up_bound=3.25),\n", "}\n", "start_time = datetime(2021, 1, 1)\n", "end_time = datetime(2024, 6, 1)\n", "\n", "start_time = datetime(2024, 6, 1)\n", "end_time = datetime(2025, 8, 1)\n", "\n", "\n", "# --- 1. 初始化数据管理器 ---\n", "print(\"初始化数据管理器...\")\n", "data_manager = DataManager(file_path=data_file_path, symbol=global_config['symbol'], start_time=start_time, end_time=end_time)\n", "# 确保 DataManager 能够重置以进行多次回测\n", "# data_manager.reset() # 首次运行不需要重置\n", "\n", "# --- 2. 初始化回测引擎并运行 ---\n", "print(\"\\n初始化回测引擎...\")\n", "engine = BacktestEngine(\n", " data_manager=data_manager,\n", " strategy_class=SimpleLimitBuyStrategy,\n", " # current_segment_symbol=strategy_parameters['symbol'],\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,\n", " start_time=start_time,\n", " end_time=end_time,\n", " indicators=INDICATOR_LIST\n", ")\n", "\n", "print(\"\\n开始运行回测...\")\n", "engine.run_backtest()\n", "print(\"\\n回测运行完毕。\")\n", "\n", "# --- 3. 获取回测结果 ---\n", "results = engine.get_backtest_results()\n", "portfolio_snapshots = results[\"portfolio_snapshots\"]\n", "trade_history = results[\"trade_history\"]\n", "initial_capital_result = results[\"initial_capital\"]\n", "bars = results[\"all_bars\"]\n", "\n", "# --- 4. 结果分析与可视化 ---\n", "if portfolio_snapshots:\n", " analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result, INDICATOR_LIST)\n", "\n", " analyzer.generate_report()\n", " analyzer.plot_performance()\n", " metrics = analyzer.calculate_all_metrics()\n", " print(metrics)\n", "\n", " analyzer.analyze_indicators()\n", "else:\n", " print(\"\\n没有生成投资组合快照，无法进行结果分析。\")\n", "\n", "# --- 4. 结果分析与可视化 (待实现) ---\n", "# if portfolio_snapshots:\n", "# analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, initial_capital_result)\n", "# metrics = analyzer.calculate_all_metrics()\n", "# print(\"\\n--- 绩效指标 ---\")\n", "# for key, value in metrics.items():\n", "# print(f\" {key}: {value:.4f}\")\n", "#\n", "# print(\"\\n--- 绘制绩效图表 ---\")\n", "# analyzer.plot_performance()\n", "# else:\n", "# print(\"\\n没有生成投资组合快照，无法进行结果分析。\")\n", "\n" ] } ], "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 }