227 lines
9.3 KiB
Plaintext
227 lines
9.3 KiB
Plaintext
{
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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": "2025-10-21T13:27:14.609968Z",
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"start_time": "2025-10-21T13:27:14.180365Z"
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}
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},
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"source": [
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"from datetime import datetime\n",
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"\n",
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"from src.data_processing import load_raw_data\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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"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/')"
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],
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"outputs": [],
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"execution_count": 1
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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": "2025-10-21T13:27:14.989537Z",
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"start_time": "2025-10-21T13:27:14.615874Z"
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}
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},
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"source": [
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"from turtle import down\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 *\n",
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"\n",
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"# 导入您自己的 SMC 策略\n",
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"from src.strategies.TrendlineBreakoutStrategy.TrendlineHawkesStrategyFast import TrendlineHawkesStrategy\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/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min15.csv'\n"
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],
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"outputs": [],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-10-21T13:27:15.060902Z",
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"start_time": "2025-10-21T13:27:14.996119Z"
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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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"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 = 0.0000 # 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', # 确保与数据文件中的 symbol 匹配\n",
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"}\n",
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"\n",
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"# 回测时间范围\n",
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"start_time = datetime(2021, 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, 1, 1)\n",
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"end_time = datetime(2025, 8, 1)\n",
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"\n",
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"\n",
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"indicators = INDICATOR_LIST\n",
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"indicators = []\n",
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"\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"
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],
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"id": "9ee53c41eaaefabb",
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-10-21T13:27:21.876001Z",
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"start_time": "2025-10-21T13:27:15.070471Z"
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}
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},
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"cell_type": "code",
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"source": [
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"from src.indicators.indicators import ROC_MA\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,\n",
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" end_time=end_time)\n",
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"\n",
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"strategy_parameters = {\n",
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" 'main_symbol': 'MA', # <-- 替换为你的交易品种代码，例如 'GC=F' (黄金期货), 'ZC=F' (玉米期货)\n",
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" 'trade_volume': 1,\n",
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" 'trendline_n': 70,\n",
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" 'hawkes_kappa': 0.1,\n",
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" 'order_direction': ['SELL', 'BUY'],\n",
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" 'reverse_logic': True,\n",
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" # 'indicators': [RateOfChange(10, -2.1, -0.5), ROC_MA(10, 10, -2.7, -0.4)],\n",
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" 'enable_log': False\n",
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"}\n",
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"\n",
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"\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=TrendlineHawkesStrategy, # <--- 更改为您的 SMC 策略类\n",
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" # current_segment_symbol 参数已从 SMCPureH1LongStrategy 中移除，不需要设置\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=indicators # 如果您的 SMC 策略不使用这些指标，也可以考虑移除\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。\")"
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],
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"id": "f903fd2761d446cd",
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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_min15.csv\n",
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"数据范围从 2020-12-31 14:45:00 到 2025-08-21 14:30:00\n",
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"总计 25596 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=0.0\n",
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"内存仓储已初始化，管理ID: 'src.strategies.TrendlineBreakoutStrategy.TrendlineHawkesStrategyFast.TrendlineHawkesStrategy_13b1be9c188912b2ee8ccd9e5fb0718d'\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: TrendlineHawkesStrategy\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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"TrendlineHawkesStrategy 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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"首次运行，正在初始化霍克斯状态和滚动窗口...\n",
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"状态初始化完成。\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 38\u001B[39m\n\u001B[32m 23\u001B[39m engine = BacktestEngine(\n\u001B[32m 24\u001B[39m data_manager=data_manager,\n\u001B[32m 25\u001B[39m strategy_class=TrendlineHawkesStrategy, \u001B[38;5;66;03m# <--- 更改为您的 SMC 策略类\u001B[39;00m\n\u001B[32m (...)\u001B[39m\u001B[32m 34\u001B[39m indicators=indicators \u001B[38;5;66;03m# 如果您的 SMC 策略不使用这些指标，也可以考虑移除\u001B[39;00m\n\u001B[32m 35\u001B[39m )\n\u001B[32m 37\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m开始运行回测...\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m38\u001B[39m \u001B[43mengine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrun_backtest\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 39\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m回测运行完毕。\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 41\u001B[39m \u001B[38;5;66;03m# --- 3. 获取回测结果 ---\u001B[39;00m\n",
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"\u001B[36mFile \u001B[39m\u001B[32m/mnt/d/PyProject/NewQuant/src/backtest_engine.py:166\u001B[39m, in \u001B[36mBacktestEngine.run_backtest\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 163\u001B[39m \u001B[38;5;28mself\u001B[39m.strategy.on_open_bar(current_bar.open, current_bar.symbol)\n\u001B[32m 165\u001B[39m current_indicator_dict = {}\n\u001B[32m--> \u001B[39m\u001B[32m166\u001B[39m close_array = \u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marray\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mclose_list\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 167\u001B[39m open_array = np.array(\u001B[38;5;28mself\u001B[39m.open_list)\n\u001B[32m 168\u001B[39m high_array = np.array(\u001B[38;5;28mself\u001B[39m.high_list)\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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"execution_count": 4
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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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