192 lines
6.9 KiB
Plaintext
192 lines
6.9 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": "initial_id",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-12T14:39:30.843472Z",
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"start_time": "2025-07-12T14:39:30.823755Z"
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},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"%load_ext autoreload\n",
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"%autoreload 2\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": null,
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"id": "a559dfcf",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-12T14:39:35.610573Z",
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"start_time": "2025-07-12T14:39:30.855663Z"
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}
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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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"初始化数据管理器...\n",
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"数据加载成功: /mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min60.csv\n",
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"数据范围从 2021-12-31 14:00:00 到 2025-07-10 09:00:00\n",
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"总计 5904 条记录。\n",
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"\n",
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"初始化回测引擎...\n",
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"模拟器初始化：初始资金=100000.00, 滑点率=0.0, 佣金率=0.0001\n",
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"\n",
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"--- 回测引擎初始化完成 ---\n",
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" 策略: SimpleLimitBuyStrategyShort\n",
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" 初始资金: 100000.00\n",
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" 换月模式: 启用\n",
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"\n",
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"开始运行回测...\n",
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"\n",
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"--- 回测开始 ---\n",
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"SimpleLimitBuyStrategyShort 策略初始化回调被调用。\n",
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"开始将 DataFrame 转换为 Bar 对象流...\n"
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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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"from src.analysis.result_analyzer import ResultAnalyzer\n",
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"# 导入所有必要的模块\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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"from src.indicators.indicator_list import INDICATOR_LIST\n",
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"from src.indicators.indicators import RSI, HistoricalRange, StochasticOscillator\n",
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"from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategy\n",
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"\n",
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"\n",
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"# --- 配置参数 ---\n",
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"# 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",
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"# data_file_path = '/mnt/d/PyProject/NewQuant/data/data/SHFE_rb2510/SHFE_rb2510_min60.csv'\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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"\n",
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"initial_capital = 100000.0\n",
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"slippage_rate = 0.000 # 假设每笔交易0.1%的滑点\n",
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"commission_rate = 0.0001 # 假设每笔交易0.02%的佣金\n",
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"\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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"\n",
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"strategy_parameters = {\n",
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" # 'symbol': \"SHFE_rb2501\", # 根据您的数据文件中的品种名称调整\n",
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" 'trade_volume': 1,\n",
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" # 'range_factor': 1.8, # 示例值，需要通过网格搜索优化\n",
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" # 'profit_factor': 2.3, # 示例值\n",
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" 'range_factor': 1.6, # 示例值，需要通过网格搜索优化\n",
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" 'profit_factor': 2.1, # 示例值\n",
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" # 'range_factor_l': 1.8, # 示例值，需要通过网格搜索优化\n",
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" # 'profit_factor_l': 2.8, # 示例值\n",
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" # 'range_factor_s': 1.6, # 示例值，需要通过网格搜索优化\n",
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" # 'profit_factor_s': 2.1, # 示例值\n",
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" 'max_position': 10,\n",
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" 'enable_log': False,\n",
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" 'stop_loss_points': 15,\n",
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" 'use_indicator': True,\n",
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" # 'indicator': HistoricalRange(shift_window=20, down_bound=10, up_bound=24),\n",
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" 'indicator': RSI(5, 15, 60),\n",
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" # 'indicator_l': HistoricalRange(21, 10, 25),\n",
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" # 'indicator_s': RSI(5, 20, 60),\n",
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"}\n",
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"start_time = datetime(2022, 1, 1)\n",
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"end_time = datetime(2024, 6, 1)\n",
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"\n",
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"# start_time = datetime(2024, 6, 1)\n",
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"# end_time = datetime(2025, 6, 1)\n",
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"\n",
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"\n",
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"# --- 1. 初始化数据管理器 ---\n",
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"print(\"初始化数据管理器...\")\n",
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"data_manager = DataManager(file_path=data_file_path, symbol=global_config['symbol'], start_time=start_time, end_time=end_time)\n",
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"# 确保 DataManager 能够重置以进行多次回测\n",
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"# data_manager.reset() # 首次运行不需要重置\n",
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"\n",
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"# --- 2. 初始化回测引擎并运行 ---\n",
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"print(\"\\n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=SimpleLimitBuyStrategyShort,\n",
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" # current_segment_symbol=strategy_parameters['symbol'],\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,\n",
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" start_time=start_time,\n",
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" end_time=end_time,\n",
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" indicators=INDICATOR_LIST\n",
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")\n",
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"\n",
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"print(\"\\n开始运行回测...\")\n",
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"engine.run_backtest()\n",
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"print(\"\\n回测运行完毕。\")\n",
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"\n",
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"# --- 3. 获取回测结果 ---\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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"initial_capital_result = results[\"initial_capital\"]\n",
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"bars = results[\"all_bars\"]\n",
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"\n",
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"# --- 4. 结果分析与可视化 ---\n",
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"if portfolio_snapshots:\n",
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" analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result, INDICATOR_LIST)\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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" print(metrics)\n",
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"\n",
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" analyzer.analyze_indicators()\n",
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"else:\n",
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" print(\"\\n没有生成投资组合快照，无法进行结果分析。\")\n",
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"\n",
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"# --- 4. 结果分析与可视化 (待实现) ---\n",
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"# if portfolio_snapshots:\n",
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"# analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, initial_capital_result)\n",
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"# metrics = analyzer.calculate_all_metrics()\n",
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"# print(\"\\n--- 绩效指标 ---\")\n",
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"# for key, value in metrics.items():\n",
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"# print(f\" {key}: {value:.4f}\")\n",
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"#\n",
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"# print(\"\\n--- 绘制绩效图表 ---\")\n",
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"# analyzer.plot_performance()\n",
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"# else:\n",
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"# print(\"\\n没有生成投资组合快照，无法进行结果分析。\")\n",
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"\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "quant",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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