diff --git a/5.ipynb b/5.ipynb
new file mode 100644
index 0000000..c21f79d
--- /dev/null
+++ b/5.ipynb
@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b0c9ccef-ed78-460b-ada2-2ee70be45019",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "def num_gen(n):\n",
+    "    for i in range(n):\n",
+    "        yield i\n",
+    "for num in num_gen(5):\n",
+    "    print(num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "422c3d89-83e2-4f54-acaf-9f03bc938beb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/portfolio_analysis.py b/portfolio_analysis.py
new file mode 100644
index 0000000..32cf574
--- /dev/null
+++ b/portfolio_analysis.py
@@ -0,0 +1,1783 @@
+import yfinance as yf
+import akshare as ak
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+from datetime import datetime, timedelta
+import os
+import warnings
+
+warnings.filterwarnings('ignore')
+
+# 设置中文字体
+matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
+matplotlib.rcParams['axes.unicode_minus'] = False
+
+
+def create_output_directory():
+    """创建输出目录"""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_dir = f"portfolio_analysis_{timestamp}"
+
+    # 创建主目录
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # 创建子目录
+    subdirs = ['tables', 'charts', 'data']
+    for subdir in subdirs:
+        subdir_path = os.path.join(output_dir, subdir)
+        if not os.path.exists(subdir_path):
+            os.makedirs(subdir_path)
+
+    print(f"输出目录已创建: {output_dir}/")
+    return output_dir
+
+
+def get_a_stock_data(symbol='515450', years=5):
+    """
+    使用AKShare获取A股515450近五年的数据
+    """
+    print(f"正在通过AKShare获取A股 {symbol} 近{years}年数据...")
+
+    # 计算5年前的日期
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=years * 365)
+
+    # 格式化日期
+    start_str = start_date.strftime('%Y%m%d')
+    end_str = end_date.strftime('%Y%m%d')
+
+    try:
+        print("尝试使用ETF基金接口获取A股数据...")
+
+        # 方法1：尝试使用ETF基金接口
+        try:
+            df = ak.fund_etf_hist_em(symbol=symbol, period="daily",
+                                     start_date=start_str, end_date=end_str,
+                                     adjust="qfq")
+            print("使用ETF基金接口成功")
+        except Exception as e1:
+            print(f"ETF基金接口失败: {e1}")
+
+            # 方法2：尝试使用股票接口
+            print("尝试使用股票接口...")
+            try:
+                # 尝试不同的后缀
+                for suffix in ['.SZ', '.SS', '']:
+                    try:
+                        stock_symbol = f"{symbol}{suffix}" if suffix else symbol
+                        df = ak.stock_zh_a_hist(symbol=stock_symbol, period="daily",
+                                                start_date=start_str, end_date=end_str,
+                                                adjust="qfq")
+                        print(f"使用股票接口成功: {stock_symbol}")
+                        break
+                    except:
+                        continue
+            except Exception as e2:
+                print(f"股票接口失败: {e2}")
+                raise ValueError(f"无法通过AKShare获取A股{symbol}数据")
+
+        if df.empty:
+            raise ValueError(f"未能获取到A股 {symbol} 的数据")
+
+        print(f"获取到 {len(df)} 行数据")
+
+        # 重命名列
+        column_mapping = {
+            '日期': 'Date',
+            '时间': 'Date',
+            '收盘': 'Close',
+            '收盘价': 'Close',
+            'price': 'Close',
+            '最新价': 'Close'
+        }
+
+        for old_col, new_col in column_mapping.items():
+            if old_col in df.columns:
+                df = df.rename(columns={old_col: new_col})
+
+        # 确保Date列是datetime类型
+        if 'Date' in df.columns:
+            df['Date'] = pd.to_datetime(df['Date'])
+            df = df.set_index('Date')
+        else:
+            # 如果没有Date列，使用索引
+            df.index = pd.to_datetime(df.index)
+
+        # 按日期排序
+        df = df.sort_index()
+
+        # 提取收盘价
+        close_price = None
+        for col in ['Close', '收盘', '收盘价', 'price', '最新价']:
+            if col in df.columns:
+                close_price = df[col]
+                print(f"使用 '{col}' 列作为收盘价")
+                break
+
+        if close_price is None and len(df.columns) > 0:
+            print("警告: 未找到标准收盘价列，尝试使用第一列数值列")
+            for col in df.columns:
+                if pd.api.types.is_numeric_dtype(df[col]):
+                    close_price = df[col]
+                    print(f"使用 '{col}' 列作为收盘价")
+                    break
+
+        if close_price is None:
+            raise ValueError("无法提取收盘价数据")
+
+        # 转换为数值类型并处理缺失值
+        close_price = pd.to_numeric(close_price, errors='coerce')
+        close_price = close_price.ffill().bfill()
+
+        # 移除时区信息
+        close_price.index = close_price.index.tz_localize(None)
+
+        print(f"A股 {symbol} 数据获取成功!")
+        print(f"数据时间范围: {close_price.index[0].date()} 到 {close_price.index[-1].date()}")
+        print(f"总交易日数: {len(close_price)}")
+
+        return close_price
+
+    except Exception as e:
+        print(f"通过AKShare获取A股 {symbol} 数据时出错: {e}")
+        raise
+
+
+def get_us_stock_data(symbol='QLD', years=5):
+    """
+    使用yfinance获取美股数据（使用QLD）
+    """
+    print(f"正在通过yfinance获取美股 {symbol} 近{years}年数据...")
+
+    # 计算5年前的日期
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=years * 365)
+
+    # 格式化日期为字符串
+    start_str = start_date.strftime('%Y-%m-%d')
+    end_str = end_date.strftime('%Y-%m-%d')
+
+    print(f"时间范围: {start_str} 到 {end_str}")
+
+    try:
+        # 创建Ticker对象
+        ticker = yf.Ticker(symbol)
+
+        # 获取历史数据
+        stock_data = ticker.history(start=start_str, end=end_str)
+
+        if stock_data.empty:
+            print(f"警告: {symbol} 数据为空，尝试获取最大历史数据...")
+            stock_data = ticker.history(period="max")
+
+            if stock_data.empty:
+                raise ValueError(f"未能获取到美股 {symbol} 的数据")
+
+            # 截取最近5年的数据
+            cutoff_date = end_date - timedelta(days=years * 365)
+            stock_data = stock_data[stock_data.index >= pd.Timestamp(cutoff_date)]
+            print("使用最大历史数据截取成功")
+
+        print(f"美股 {symbol} 数据下载成功!")
+        print(
+            f"数据时间范围: {stock_data.index[0].date()} 到 {stock_data.index[-1].date()}")  # 修复这里：使用stock_data而不是stock_prices
+        print(f"总交易日数: {len(stock_data)}")
+
+        # 提取收盘价
+        close_price = None
+
+        # 优先使用调整后收盘价
+        if 'Adj Close' in stock_data.columns:
+            close_price = stock_data['Adj Close']
+            print("使用 'Adj Close' 列作为收盘价")
+        elif 'Close' in stock_data.columns:
+            close_price = stock_data['Close']
+            print("使用 'Close' 列作为收盘价")
+        else:
+            # 尝试寻找包含'Close'的列
+            for col in stock_data.columns:
+                if 'close' in str(col).lower():
+                    close_price = stock_data[col]
+                    print(f"使用 '{col}' 列作为收盘价")
+                    break
+
+        if close_price is None:
+            print("警告: 未找到标准收盘价列，尝试使用第一列数值列")
+            for col in stock_data.columns:
+                if pd.api.types.is_numeric_dtype(stock_data[col]):
+                    close_price = stock_data[col]
+                    print(f"使用 '{col}' 列作为收盘价")
+                    break
+
+        if close_price is None:
+            raise ValueError("无法提取收盘价数据")
+
+        # 转换为数值类型并处理缺失值
+        close_price = pd.to_numeric(close_price, errors='coerce')
+        close_price = close_price.ffill().bfill()
+
+        # 移除时区信息
+        close_price.index = close_price.index.tz_localize(None)
+
+        return close_price
+
+    except Exception as e:
+        print(f"通过yfinance获取美股 {symbol} 数据时出错: {e}")
+        raise
+
+
+def get_exchange_rate_data(years=5):
+    """
+    获取人民币兑美元汇率数据（无参数版本）
+    """
+    print("正在获取人民币兑美元汇率数据...")
+
+    # 计算日期范围
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=years * 365)
+
+    try:
+        # 直接调用函数，无任何参数
+        df = ak.currency_boc_safe()
+
+        if df.empty:
+            raise ValueError("未能获取到汇率数据")
+
+        print(f"获取到 {len(df)} 条汇率数据")
+        print(f"数据列名: {df.columns.tolist()}")
+        print(f"数据示例:\n{df.head()}")
+
+        # 检查数据列名，根据实际列名重命名
+        # 注意：列名可能是中文的
+        column_mapping = {}
+
+        # 查找日期列
+        date_columns = ['日期', 'Date', 'date', 'time', '交易日', '交易日期']
+        for col in date_columns:
+            if col in df.columns:
+                column_mapping[col] = 'Date'
+                break
+
+        # 查找汇率列（美元兑人民币中间价）
+        rate_columns = ['美元', 'USD', '中间价', '现汇卖出价', '现汇买入价', 'price', 'rate', '汇率']
+        for col in rate_columns:
+            if col in df.columns:
+                column_mapping[col] = 'Exchange_Rate'
+                break
+
+        # 如果找到了需要重命名的列，执行重命名
+        if column_mapping:
+            df = df.rename(columns=column_mapping)
+            print(f"已将列名映射为: {column_mapping}")
+
+        # 确保有Date列
+        if 'Date' not in df.columns and len(df.columns) > 0:
+            # 使用第一列作为日期
+            first_col = df.columns[0]
+            df = df.rename(columns={first_col: 'Date'})
+            print(f"将第一列 '{first_col}' 作为Date列")
+
+        # 确保有Exchange_Rate列
+        if 'Exchange_Rate' not in df.columns and len(df.columns) > 1:
+            # 使用第二列作为汇率
+            second_col = df.columns[1]
+            df = df.rename(columns={second_col: 'Exchange_Rate'})
+            print(f"将第二列 '{second_col}' 作为Exchange_Rate列")
+
+        # 转换为datetime类型
+        df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
+        df = df.dropna(subset=['Date'])
+        df = df.set_index('Date')
+        df = df.sort_index()
+
+        # 提取汇率数据
+        if 'Exchange_Rate' in df.columns:
+            exchange_rate = df['Exchange_Rate']
+        else:
+            # 如果没有Exchange_Rate列，尝试使用第一个数值列
+            for col in df.columns:
+                if pd.api.types.is_numeric_dtype(df[col]):
+                    exchange_rate = df[col]
+                    print(f"使用数值列 '{col}' 作为汇率数据")
+                    break
+            else:
+                raise ValueError("找不到汇率数据列")
+
+        # 转换为数值类型
+        exchange_rate = pd.to_numeric(exchange_rate, errors='coerce')
+        exchange_rate = exchange_rate.ffill().bfill()
+
+        # 筛选指定日期范围
+        exchange_rate = exchange_rate[(exchange_rate.index >= start_date) &
+                                      (exchange_rate.index <= end_date)]
+
+        if len(exchange_rate) == 0:
+            print("警告: 指定日期范围内无汇率数据，使用全部数据")
+            exchange_rate = exchange_rate
+
+        print(f"汇率数据获取成功!")
+        print(f"数据时间范围: {exchange_rate.index[0].date()} 到 {exchange_rate.index[-1].date()}")
+        print(f"可用数据点数: {len(exchange_rate)}")
+        print(f"最新汇率: 1美元 = {exchange_rate.iloc[-1]:.4f} 人民币")
+
+        # 如果需要，可以插值生成每日数据
+        if len(exchange_rate) < years * 365 * 0.5:  # 如果数据点少于预期的一半
+            print("数据点较少，生成完整日期序列...")
+            full_index = pd.date_range(start=start_date, end=end_date, freq='D')
+            exchange_rate = exchange_rate.reindex(full_index)
+            exchange_rate = exchange_rate.ffill().bfill()
+            print(f"插值后数据点数: {len(exchange_rate)}")
+
+        return exchange_rate
+
+    except Exception as e:
+        print(f"获取汇率数据时出错: {e}")
+        import traceback
+        traceback.print_exc()
+        print("将使用固定汇率7.1进行估算")
+        # 创建完整的日期序列
+        all_dates = pd.date_range(start=start_date, end=end_date, freq='D')
+        exchange_rate = pd.Series(7.1, index=all_dates, name='Exchange_Rate')
+        return exchange_rate
+
+
+def get_stock_data():
+    """
+    获取A股515450和美股QLD近五年的收盘价数据，以及汇率数据
+    """
+    print("=" * 80)
+    print("获取近五年A股515450与美股QLD数据")
+    print("=" * 80)
+
+    all_data = {}
+
+    try:
+        # 获取A股515450数据
+        print("\n1. 获取A股515450数据...")
+        a_stock_price = get_a_stock_data('515450', years=5)
+        a_stock_price.name = 'A股_515450'
+        all_data['A股_515450'] = a_stock_price
+
+        # 获取美股QLD数据
+        print("\n2. 获取美股QLD数据...")
+        us_stock_price = get_us_stock_data('QLD', years=5)
+        us_stock_price.name = '美股_QLD'
+        all_data['美股_QLD'] = us_stock_price
+
+        # 获取汇率数据
+        print("\n3. 获取汇率数据...")
+        exchange_rate = get_exchange_rate_data(years=5)
+        all_data['汇率_USD_CNY'] = exchange_rate
+
+        # 创建空的DataFrame
+        combined_data = pd.DataFrame()
+
+        # 逐个添加数据
+        for name, data in all_data.items():
+            temp_df = pd.DataFrame({name: data})
+
+            if combined_data.empty:
+                combined_data = temp_df
+            else:
+                combined_data = combined_data.merge(temp_df,
+                                                    left_index=True,
+                                                    right_index=True,
+                                                    how='outer')
+
+        # 按日期排序
+        combined_data = combined_data.sort_index()
+
+        # 处理缺失值 - 前向填充和后向填充
+        combined_data = combined_data.ffill().bfill()
+        combined_data = combined_data.dropna()
+
+    except Exception as e:
+        print(f"\n数据获取失败: {e}")
+        raise
+
+    if combined_data.empty:
+        raise ValueError("数据合并后为空，请检查数据源")
+
+    # 计算实际的时间跨度
+    start_date = combined_data.index[0]
+    end_date = combined_data.index[-1]
+    actual_days = (end_date - start_date).days
+    actual_years = actual_days / 365.25
+
+    print(f"\n数据获取完成!")
+    print(f"总数据行数: {len(combined_data)}")
+    print(f"实际时间范围: {start_date.date()} 到 {end_date.date()}")
+    print(f"实际时间跨度: {actual_days} 天 ({actual_years:.2f} 年)")
+    print(f"可用数据列: {list(combined_data.columns)}")
+
+    return combined_data
+
+
+def get_user_weights(asset_names):
+    """
+    获取用户输入的投资权重
+    """
+    print("\n" + "=" * 60)
+    print("投资组合权重配置")
+    print("=" * 60)
+
+    # 过滤掉汇率列
+    filtered_assets = [asset for asset in asset_names if '汇率' not in asset]
+    print(f"可用资产: {', '.join(filtered_assets)}")
+    print("请输入每个资产的权重百分比（总和应为100%）")
+
+    weights = {}
+
+    while True:
+        try:
+            total_weight = 0
+
+            for i, asset in enumerate(filtered_assets):
+                if i == 0:
+                    prompt = f"请输入 {asset} 的权重百分比 (0-100): "
+                else:
+                    remaining = 100 - total_weight
+                    prompt = f"请输入 {asset} 的权重百分比 (剩余 {remaining}%): "
+
+                weight_input = input(prompt).strip()
+
+                if not weight_input:
+                    raise ValueError("请输入一个有效的数字")
+
+                weight = float(weight_input)
+
+                if weight < 0 or weight > 100:
+                    raise ValueError("权重必须在 0 到 100 之间")
+
+                if total_weight + weight > 100:
+                    raise ValueError(f"权重总和不能超过100%，当前已输入: {total_weight}%")
+
+                weights[asset] = weight / 100
+                total_weight += weight
+
+            # 检查权重总和
+            if abs(total_weight - 100) < 0.01:
+                break
+            elif total_weight < 100:
+                response = input(
+                    f"\n权重总和为 {total_weight}%，未达到100%。是否将剩余的 {100 - total_weight}% 分配给最后一个资产? (y/n): ").lower()
+                if response == 'y':
+                    last_asset = filtered_assets[-1]
+                    weights[last_asset] += (100 - total_weight) / 100
+                    break
+                else:
+                    print("请重新配置权重")
+                    weights = {}
+                    continue
+            else:
+                print(f"权重总和 {total_weight}% 超过100%，请重新配置")
+                weights = {}
+                continue
+
+        except ValueError as e:
+            print(f"输入错误: {e}")
+            print("请重新输入")
+            weights = {}
+
+    # 显示配置结果
+    print("\n投资组合配置结果:")
+    print("-" * 40)
+    for asset, weight in weights.items():
+        print(f"{asset}: {weight * 100:.1f}%")
+
+    # 转换为列表格式
+    weight_list = [weights[asset] for asset in filtered_assets]
+
+    return weight_list, filtered_assets
+
+
+def get_investment_plan():
+    """
+    获取用户的投资计划
+    """
+    print("\n" + "=" * 60)
+    print("投资计划配置")
+    print("=" * 60)
+
+    while True:
+        try:
+            # 获取初始本金
+            principal_input = input("请输入初始本金金额(人民币): ").strip()
+            if not principal_input:
+                principal = 10000.0
+                print(f"使用默认本金: {principal:.2f} 元")
+            else:
+                principal = float(principal_input)
+                if principal <= 0:
+                    raise ValueError("本金必须大于0")
+
+            # 获取每月定投金额
+            monthly_input = input("请输入每月定投金额(人民币，输入0表示不定投): ").strip()
+            if not monthly_input:
+                monthly_investment = 0.0
+                print(f"使用默认值: 无每月定投")
+            else:
+                monthly_investment = float(monthly_input)
+                if monthly_investment < 0:
+                    raise ValueError("每月投资金额不能为负数")
+
+            # 获取投资起始日期
+            start_date_input = input("请输入投资起始日期(YYYY-MM-DD，留空使用数据起始日期): ").strip()
+            if not start_date_input:
+                start_date = None
+                print("使用数据起始日期")
+            else:
+                start_date = pd.to_datetime(start_date_input)
+
+            break
+
+        except ValueError as e:
+            print(f"输入错误: {e}")
+            print("请重新输入")
+        except Exception as e:
+            print(f"输入错误: {e}")
+            print("请重新输入")
+
+    return {
+        'initial_principal': principal,
+        'monthly_investment': monthly_investment,
+        'start_date': start_date
+    }
+
+
+def calculate_investment_simulation(prices, weights, asset_names, investment_plan):
+    """
+    计算投资模拟结果（完全重构版）- 正确计算份额和收益
+    """
+    print("\n计算投资模拟结果...")
+
+    # 提取股票价格和汇率
+    stock_prices = prices[asset_names].copy()
+    exchange_rate = prices['汇率_USD_CNY'].copy()
+
+    # 确定投资起始日期
+    if investment_plan['start_date']:
+        start_date = pd.Timestamp(investment_plan['start_date'])
+        if start_date < stock_prices.index[0]:
+            print(f"警告: 指定起始日期 {start_date.date()} 早于数据起始日期 {stock_prices.index[0].date()}")
+            print(f"将使用数据起始日期: {stock_prices.index[0].date()}")
+            start_date = stock_prices.index[0]
+    else:
+        start_date = stock_prices.index[0]
+
+    # 筛选投资期间的数据
+    investment_prices = stock_prices[stock_prices.index >= start_date].copy()
+    investment_exchange = exchange_rate[exchange_rate.index >= start_date].copy()
+
+    if investment_prices.empty:
+        raise ValueError("投资起始日期后无有效数据")
+
+    print(f"投资起始日期: {start_date.date()}")
+    print(f"投资结束日期: {investment_prices.index[-1].date()}")
+    print(f"投资天数: {len(investment_prices)}")
+
+    # === 重新设计：简化但正确的投资模拟 ===
+
+    # 1. 计算每日投资组合的人民币价值
+    portfolio_daily_value_cny = pd.Series(0.0, index=investment_prices.index)
+
+    for i, asset in enumerate(asset_names):
+        asset_weight = weights[i]
+
+        if '美股' in asset:
+            # 美股：美元价格 × 汇率 = 人民币价格
+            usd_price = investment_prices[asset]
+            # 对齐汇率和价格的索引
+            aligned_exchange = investment_exchange.reindex(usd_price.index).ffill().bfill()
+            cny_price = usd_price * aligned_exchange
+            portfolio_daily_value_cny += cny_price * asset_weight
+        else:
+            # A股：直接使用人民币价格
+            portfolio_daily_value_cny += investment_prices[asset] * asset_weight
+
+    print(f"投资组合初始价值（理论）: {portfolio_daily_value_cny.iloc[0]:.2f} 元")
+    print(f"投资组合最终价值（理论）: {portfolio_daily_value_cny.iloc[-1]:.2f} 元")
+    print(f"投资组合总增长率: {portfolio_daily_value_cny.iloc[-1] / portfolio_daily_value_cny.iloc[0]:.4f}倍")
+
+    # 2. 计算投资组合的日收益率
+    portfolio_daily_returns = portfolio_daily_value_cny.pct_change().fillna(0)
+
+    # 3. 模拟实际投资过程（更直观的方法）
+    initial_principal = investment_plan['initial_principal']
+    monthly_investment = investment_plan['monthly_investment']
+
+    # 初始化投资记录
+    investment_records = pd.DataFrame(index=investment_prices.index)
+    investment_records['投资组合价值'] = portfolio_daily_value_cny
+    investment_records['投资组合日收益率'] = portfolio_daily_returns
+    investment_records['现金投入_累计'] = 0.0
+    investment_records['持有资产价值'] = 0.0
+    investment_records['累计收益率'] = 0.0
+
+    # 4. 处理初始投资
+    first_date = investment_records.index[0]
+
+    # 假设初始投资时购买1单位投资组合
+    initial_investment_ratio = initial_principal / portfolio_daily_value_cny.iloc[0]
+
+    investment_records.loc[first_date, '现金投入_累计'] = initial_principal
+    investment_records.loc[first_date, '持有资产价值'] = initial_principal  # 初始时资产价值等于投入本金
+
+    # 5. 处理每月定投
+    cash_investments = {first_date: initial_principal}  # 记录每笔现金投入
+
+    if monthly_investment > 0:
+        # 找到每月第一个交易日进行定投
+        all_dates = investment_records.index
+
+        # 按月份分组，取每月的第一个交易日
+        monthly_first_dates = []
+        current_month = None
+
+        for date in all_dates:
+            month_key = (date.year, date.month)
+            if month_key != current_month:
+                monthly_first_dates.append(date)
+                current_month = month_key
+
+        # 排除第一个月（已经处理了初始投资）
+        if monthly_first_dates and monthly_first_dates[0] == first_date:
+            monthly_first_dates = monthly_first_dates[1:]
+
+        print(f"定投月份数（除首月）: {len(monthly_first_dates)}")
+
+        for invest_date in monthly_first_dates:
+            cash_investments[invest_date] = monthly_investment
+
+    # 6. 计算每日的资产价值
+    # 先按时间顺序处理所有投资
+    sorted_dates = sorted(cash_investments.keys())
+
+    # 初始化变量
+    total_cash_invested = 0
+    current_asset_value = 0
+
+    for i, date in enumerate(investment_records.index):
+        # 检查今天是否有现金投入
+        if date in cash_investments:
+            cash_today = cash_investments[date]
+            total_cash_invested += cash_today
+
+            # 如果有现有资产，先计算到今天的增长
+            if i > 0 and current_asset_value > 0:
+                prev_date = investment_records.index[i - 1]
+                days_return = portfolio_daily_value_cny.iloc[i] / portfolio_daily_value_cny.iloc[i - 1] - 1
+                current_asset_value *= (1 + days_return)
+
+            # 新投入的现金按当前投资组合价值折算成资产
+            investment_ratio_today = cash_today / portfolio_daily_value_cny.iloc[i]
+            current_asset_value += cash_today  # 简化：直接增加资产价值
+
+            # 记录
+            investment_records.loc[date, '现金投入_累计'] = total_cash_invested
+            investment_records.loc[date, '持有资产价值'] = current_asset_value
+
+            # 如果是当天有投入，需要重新计算收益率
+            if current_asset_value > 0 and total_cash_invested > 0:
+                investment_records.loc[date, '累计收益率'] = (current_asset_value / total_cash_invested - 1)
+
+        else:
+            # 没有现金投入的日子，只计算资产价值变化
+            if i > 0:
+                # 计算从昨天到今天投资组合的增长
+                prev_date = investment_records.index[i - 1]
+                days_return = portfolio_daily_value_cny.iloc[i] / portfolio_daily_value_cny.iloc[i - 1] - 1
+
+                # 更新资产价值
+                if current_asset_value > 0:
+                    current_asset_value *= (1 + days_return)
+
+                # 记录
+                investment_records.loc[date, '现金投入_累计'] = total_cash_invested
+                investment_records.loc[date, '持有资产价值'] = current_asset_value
+
+                # 计算累计收益率
+                if current_asset_value > 0 and total_cash_invested > 0:
+                    investment_records.loc[date, '累计收益率'] = (current_asset_value / total_cash_invested - 1)
+
+    # 7. 前向填充缺失值
+    investment_records['现金投入_累计'] = investment_records['现金投入_累计'].ffill()
+    investment_records['持有资产价值'] = investment_records['持有资产价值'].ffill()
+    investment_records['累计收益率'] = investment_records['累计收益率'].ffill()
+
+    # 8. 计算汇总指标
+    total_invested = investment_records['现金投入_累计'].iloc[-1]
+    final_value = investment_records['持有资产价值'].iloc[-1]
+    total_profit = final_value - total_invested
+    total_return_pct = (final_value / total_invested - 1) * 100 if total_invested > 0 else 0
+
+    # 计算年化收益率
+    days_invested = (investment_records.index[-1] - investment_records.index[0]).days
+    years_invested = days_invested / 365.25
+    if years_invested > 0 and total_invested > 0:
+        annualized_return = ((final_value / total_invested) ** (1 / years_invested) - 1) * 100
+    else:
+        annualized_return = 0
+
+    # 计算各资产的投资金额和收益
+    asset_investment = {}
+    asset_final_value = {}
+    asset_returns = {}
+
+    for i, asset in enumerate(asset_names):
+        asset_weight = weights[i]
+        asset_investment[asset] = total_invested * asset_weight
+
+        # 计算该资产的最终价值
+        if '美股' in asset:
+            # 美股：需要考虑汇率变化和资产本身收益
+            initial_price = investment_prices[asset].iloc[0]
+            final_price = investment_prices[asset].iloc[-1]
+            initial_exchange = investment_exchange.iloc[0]
+            final_exchange = investment_exchange.iloc[-1]
+
+            # 美元计价的收益率
+            usd_return = final_price / initial_price - 1
+            # 汇率变化
+            exchange_return = final_exchange / initial_exchange - 1
+            # 人民币计价的综合收益率
+            total_return = (1 + usd_return) * (1 + exchange_return) - 1
+
+            asset_final_value[asset] = asset_investment[asset] * (1 + total_return)
+            asset_returns[asset] = total_return * 100
+
+            print(
+                f"  {asset}: 美元收益={usd_return * 100:.1f}%, 汇率收益={exchange_return * 100:.1f}%, 综合收益={total_return * 100:.1f}%")
+        else:
+            # A股：直接计算人民币收益
+            initial_price = investment_prices[asset].iloc[0]
+            final_price = investment_prices[asset].iloc[-1]
+            asset_return = final_price / initial_price - 1
+            asset_final_value[asset] = asset_investment[asset] * (1 + asset_return)
+            asset_returns[asset] = asset_return * 100
+
+            print(f"  {asset}: 人民币收益={asset_return * 100:.1f}%")
+
+    # 汇总结果
+    simulation_results = {
+        'investment_records': investment_records,
+        'summary': {
+            '初始本金_元': initial_principal,
+            '每月定投_元': monthly_investment,
+            '总投资额_元': total_invested,
+            '最终资产总值_元': final_value,
+            '总收益_元': total_profit,
+            '总收益率_%': total_return_pct,
+            '投资天数': days_invested,
+            '投资年数': years_invested,
+            '年化收益率_%': annualized_return,
+            '投资起始日期': start_date.date(),
+            '投资结束日期': investment_records.index[-1].date(),
+            '资产配置': {asset: f"{weights[i] * 100:.1f}%" for i, asset in enumerate(asset_names)},
+            '各资产投资金额_元': asset_investment,
+            '各资产最终价值_元': asset_final_value,
+            '各资产收益率_%': asset_returns,
+            '投资组合增长倍数': portfolio_daily_value_cny.iloc[-1] / portfolio_daily_value_cny.iloc[0],
+            '现金投资笔数': len(cash_investments)
+        }
+    }
+
+    # 打印详细的投资模拟结果
+    print(f"\n投资模拟结果详情:")
+    print(f"- 初始本金: {initial_principal:.2f} 元")
+    print(f"- 每月定投: {monthly_investment:.2f} 元")
+    print(f"- 总投资额: {total_invested:.2f} 元")
+    print(f"- 最终资产: {final_value:.2f} 元")
+    print(f"- 总收益: {total_profit:.2f} 元")
+    print(f"- 总收益率: {total_return_pct:.1f}%")
+    print(f"- 年化收益: {annualized_return:.1f}%")
+    print(f"- 投资天数: {days_invested} 天")
+    print(f"- 投资年数: {years_invested:.2f} 年")
+    print(f"- 现金投资笔数: {len(cash_investments)} 笔")
+    print(f"- 投资组合理论增长: {portfolio_daily_value_cny.iloc[-1] / portfolio_daily_value_cny.iloc[0]:.4f}倍")
+
+    return simulation_results
+
+
+def save_investment_simulation_results(simulation_results, output_dir):
+    """
+    保存投资模拟结果
+    """
+    print("保存投资模拟结果...")
+
+    # 1. 保存详细投资记录
+    records_df = simulation_results['investment_records']
+    records_file = os.path.join(output_dir, 'tables', 'investment_simulation_details.csv')
+    records_df.to_csv(records_file, encoding='utf-8-sig')
+    print(f"✓ 详细投资记录已保存: tables/investment_simulation_details.csv")
+
+    # 2. 保存投资汇总
+    summary = simulation_results['summary']
+
+    summary_data = []
+    summary_data.append({'指标': '初始本金', '数值': f"{summary['初始本金_元']:.2f} 元"})
+    summary_data.append({'指标': '每月定投金额', '数值': f"{summary['每月定投_元']:.2f} 元"})
+    summary_data.append({'指标': '总投资金额', '数值': f"{summary['总投资额_元']:.2f} 元"})
+    summary_data.append({'指标': '最终资产总值', '数值': f"{summary['最终资产总值_元']:.2f} 元"})
+    summary_data.append({'指标': '总收益', '数值': f"{summary['总收益_元']:.2f} 元"})
+    summary_data.append({'指标': '总收益率', '数值': f"{summary['总收益率_%']:.2f}%"})
+    summary_data.append({'指标': '投资天数', '数值': f"{summary['投资天数']} 天"})
+    summary_data.append({'指标': '投资年数', '数值': f"{summary['投资年数']:.2f} 年"})
+    summary_data.append({'指标': '年化收益率', '数值': f"{summary['年化收益率_%']:.2f}%"})
+    summary_data.append({'指标': '投资起始日期', '数值': str(summary['投资起始日期'])})
+    summary_data.append({'指标': '投资结束日期', '数值': str(summary['投资结束日期'])})
+    summary_data.append({'指标': '投资组合增长倍数', '数值': f"{summary['投资组合增长倍数']:.4f}"})
+    summary_data.append({'指标': '现金投资笔数', '数值': f"{summary['现金投资笔数']} 笔"})
+
+    summary_df = pd.DataFrame(summary_data)
+    summary_file = os.path.join(output_dir, 'tables', 'investment_simulation_summary.csv')
+    summary_df.to_csv(summary_file, encoding='utf-8-sig', index=False)
+    print(f"✓ 投资模拟汇总已保存: tables/investment_simulation_summary.csv")
+
+    # 3. 保存各资产明细
+    asset_details = []
+    for asset, weight in summary['资产配置'].items():
+        asset_details.append({
+            '资产名称': asset,
+            '配置权重': weight,
+            '投资金额(元)': f"{summary['各资产投资金额_元'][asset]:.2f}",
+            '最终价值(元)': f"{summary['各资产最终价值_元'][asset]:.2f}",
+            '资产收益(元)': f"{summary['各资产最终价值_元'][asset] - summary['各资产投资金额_元'][asset]:.2f}",
+            '资产收益率(%)': f"{summary['各资产收益率_%'][asset]:.2f}"
+        })
+
+    asset_details_df = pd.DataFrame(asset_details)
+    asset_file = os.path.join(output_dir, 'tables', 'investment_asset_details.csv')
+    asset_details_df.to_csv(asset_file, encoding='utf-8-sig', index=False)
+    print(f"✓ 各资产投资明细已保存: tables/investment_asset_details.csv")
+
+    return summary_df, asset_details_df
+
+
+def create_investment_charts(simulation_results, output_dir):
+    """
+    创建投资模拟图表
+    """
+    print("生成投资模拟图表...")
+
+    investment_records = simulation_results['investment_records']
+    summary = simulation_results['summary']
+
+    # 图表1: 资产增长曲线
+    plt.figure(figsize=(14, 8))
+    plt.plot(investment_records.index, investment_records['持有资产价值'] / 10000,
+             label='资产总值(万元)', color='#2e8b57', linewidth=2.5)
+    plt.plot(investment_records.index, investment_records['现金投入_累计'] / 10000,
+             label='总投资额(万元)', color='#4169e1', linewidth=2.5, linestyle='--')
+
+    plt.title(
+        f'投资资产增长曲线\n最终资产: {summary["最终资产总值_元"] / 10000:.2f}万元 | 总收益: {summary["总收益_元"] / 10000:.2f}万元',
+        fontsize=14, fontweight='bold')
+    plt.ylabel('金额 (万元)', fontsize=12)
+    plt.xlabel('日期', fontsize=12)
+    plt.legend(fontsize=11)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    growth_chart_file = os.path.join(output_dir, 'charts', '08_investment_growth.png')
+    plt.savefig(growth_chart_file, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"✓ 资产增长曲线已保存: charts/08_investment_growth.png")
+
+    # 图表2: 累计收益率曲线
+    plt.figure(figsize=(14, 8))
+    plt.plot(investment_records.index, investment_records['累计收益率'] * 100,
+             color='#ff7f0e', linewidth=2.5)
+
+    plt.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
+    plt.fill_between(investment_records.index, 0, investment_records['累计收益率'] * 100,
+                     where=investment_records['累计收益率'] * 100 >= 0,
+                     color='#ff7f0e', alpha=0.3)
+
+    plt.title(f'投资累计收益率\n总收益率: {summary["总收益率_%"]:.2f}% | 年化收益率: {summary["年化收益率_%"]:.2f}%',
+              fontsize=14, fontweight='bold')
+    plt.ylabel('累计收益率 (%)', fontsize=12)
+    plt.xlabel('日期', fontsize=12)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    returns_chart_file = os.path.join(output_dir, 'charts', '09_investment_returns.png')
+    plt.savefig(returns_chart_file, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"✓ 累计收益率曲线已保存: charts/09_investment_returns.png")
+
+    # 图表3: 投资进度图（如果每月定投）
+    if summary['每月定投_元'] > 0:
+        plt.figure(figsize=(14, 8))
+
+        # 提取每月第一天的数据
+        monthly_data = investment_records.resample('MS').first()
+
+        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 12))
+
+        # 子图1: 每月投资额和资产总值
+        ax1.bar(monthly_data.index, monthly_data['现金投入_累计'] / 10000,
+                alpha=0.6, color='#4169e1', label='累计投资额(万元)')
+        ax1.plot(monthly_data.index, monthly_data['持有资产价值'] / 10000,
+                 color='#2e8b57', linewidth=2.5, label='资产总值(万元)')
+
+        ax1.set_title('月度投资进度', fontsize=13, fontweight='bold')
+        ax1.set_ylabel('金额 (万元)', fontsize=11)
+        ax1.legend(fontsize=10)
+        ax1.grid(True, alpha=0.3)
+
+        # 子图2: 每月收益率
+        monthly_returns = monthly_data['累计收益率'].diff() * 100
+        colors = ['#2ecc71' if x >= 0 else '#e74c3c' for x in monthly_returns]
+        ax2.bar(monthly_data.index, monthly_returns, color=colors, alpha=0.7)
+
+        ax2.set_title('月度收益率', fontsize=13, fontweight='bold')
+        ax2.set_ylabel('月收益率 (%)', fontsize=11)
+        ax2.set_xlabel('日期', fontsize=11)
+        ax2.grid(True, alpha=0.3)
+
+        plt.tight_layout()
+
+        progress_chart_file = os.path.join(output_dir, 'charts', '10_investment_progress.png')
+        plt.savefig(progress_chart_file, dpi=300, bbox_inches='tight')
+        plt.close()
+        print(f"✓ 投资进度图已保存: charts/10_investment_progress.png")
+
+
+def calculate_portfolio_performance(prices, weights, asset_names):
+    """
+    计算投资组合收益和指标
+    """
+    print("\n计算投资组合表现...")
+
+    # 确保数据按日期排序
+    stock_prices = prices[asset_names].copy()
+    stock_prices = stock_prices.sort_index()
+
+    # 计算日收益率
+    returns = stock_prices.pct_change().dropna()
+
+    print(f"可用交易日数: {len(returns)}")
+
+    if len(returns) < 50:
+        print(f"注意: 只有 {len(returns)} 个交易日，但会继续分析")
+
+    # 计算投资组合收益率
+    portfolio_returns = pd.Series(0.0, index=returns.index)
+    for i, asset in enumerate(asset_names):
+        portfolio_returns += returns[asset] * weights[i]
+
+    # 计算累计收益率
+    cumulative_returns = (1 + portfolio_returns).cumprod() - 1
+
+    # 计算最大回撤
+    cumulative_value = (1 + portfolio_returns).cumprod()
+    running_max = cumulative_value.expanding().max()
+    drawdown = (cumulative_value - running_max) / running_max
+    max_drawdown = drawdown.min()
+
+    # 计算时间跨度
+    start_date = stock_prices.index[0]
+    end_date = stock_prices.index[-1]
+    total_days = (end_date - start_date).days
+    total_years = total_days / 365.25
+
+    # 计算年化收益率
+    if total_years > 0:
+        final_cumulative_return = cumulative_returns.iloc[-1]
+        if final_cumulative_return >= -1:
+            annual_return = (1 + final_cumulative_return) ** (1 / total_years) - 1
+        else:
+            annual_return = -1.0
+    else:
+        annual_return = 0.0
+
+    # 年化波动率
+    if len(portfolio_returns) >= 252:
+        annual_volatility = portfolio_returns.std() * np.sqrt(252)
+    else:
+        trading_days = len(portfolio_returns)
+        annual_volatility = portfolio_returns.std() * np.sqrt(252 * (trading_days / 252))
+
+    # 夏普比率
+    risk_free_rate = 0.02
+    if portfolio_returns.std() > 0:
+        excess_return = annual_return - risk_free_rate
+        sharpe_ratio = excess_return / annual_volatility if annual_volatility > 0 else 0
+    else:
+        sharpe_ratio = 0.0
+
+    # 索提诺比率
+    downside_returns = portfolio_returns[portfolio_returns < 0]
+    downside_std = downside_returns.std() if len(downside_returns) > 0 else 0
+    if downside_std > 0:
+        annual_downside_risk = downside_std * np.sqrt(252)
+        sortino_ratio = (annual_return - risk_free_rate) / annual_downside_risk
+    else:
+        sortino_ratio = 0.0
+
+    # 其他统计指标
+    positive_days = (portfolio_returns > 0).sum()
+    negative_days = (portfolio_returns < 0).sum()
+    win_rate = positive_days / len(portfolio_returns) if len(portfolio_returns) > 0 else 0
+
+    # 收集指标
+    metrics = {
+        '总年数': total_years,
+        '交易日数': len(portfolio_returns),
+        '年化收益率': annual_return,
+        '年化波动率': annual_volatility,
+        '夏普比率': sharpe_ratio,
+        '最大回撤': max_drawdown,
+        '索提诺比率': sortino_ratio,
+        '累计收益率': cumulative_returns.iloc[-1],
+        '起始日期': str(stock_prices.index[0].date()),
+        '结束日期': str(stock_prices.index[-1].date()),
+        '正收益天数': positive_days,
+        '负收益天数': negative_days,
+        '胜率': win_rate,
+        '平均日收益': portfolio_returns.mean(),
+        '收益标准差': portfolio_returns.std(),
+        '年化下行风险': annual_downside_risk if 'annual_downside_risk' in locals() else 0
+    }
+
+    return portfolio_returns, cumulative_returns, drawdown, metrics, returns
+
+
+def save_stock_price_table(prices, asset_names, output_dir):
+    """
+    保存股票价格表格到CSV文件
+    """
+    print("生成股票价格表格...")
+
+    stock_prices = prices[asset_names].copy()
+
+    # 1. 保存原始价格数据
+    price_file = os.path.join(output_dir, 'tables', 'stock_prices_detailed.csv')
+    stock_prices.to_csv(price_file, encoding='utf-8-sig')
+    print(f"✓ 详细价格数据已保存: tables/stock_prices_detailed.csv")
+
+    # 2. 创建汇总表格
+    summary_data = []
+
+    for asset in asset_names:
+        asset_data = stock_prices[asset]
+
+        # 基础信息
+        start_price = asset_data.iloc[0]
+        end_price = asset_data.iloc[-1]
+        max_price = asset_data.max()
+        min_price = asset_data.min()
+        avg_price = asset_data.mean()
+        std_price = asset_data.std()
+
+        # 收益率计算
+        total_return = (end_price - start_price) / start_price * 100
+        days = (stock_prices.index[-1] - stock_prices.index[0]).days
+        years = days / 365.25
+        if years > 0:
+            annual_return = ((1 + total_return / 100) ** (1 / years) - 1) * 100
+        else:
+            annual_return = 0
+
+        summary_data.append({
+            '资产名称': asset,
+            '起始价格': start_price,
+            '结束价格': end_price,
+            '总收益率%': total_return,
+            '年化收益率%': annual_return,
+            '最高价格': max_price,
+            '最低价格': min_price,
+            '平均价格': avg_price,
+            '价格标准差': std_price,
+            '数据点数': len(asset_data),
+            '起始日期': stock_prices.index[0].date(),
+            '结束日期': stock_prices.index[-1].date()
+        })
+
+    # 创建DataFrame并保存
+    summary_df = pd.DataFrame(summary_data)
+    summary_file = os.path.join(output_dir, 'tables', 'stock_prices_summary.csv')
+    summary_df.to_csv(summary_file, encoding='utf-8-sig', index=False)
+    print(f"✓ 价格汇总数据已保存: tables/stock_prices_summary.csv")
+
+    return summary_df
+
+
+def save_returns_table(prices, asset_names, returns, portfolio_returns, cumulative_returns, output_dir):
+    """
+    保存收益率表格到CSV文件
+    """
+    print("生成收益率表格...")
+
+    # 1. 保存日收益率数据
+    daily_returns_file = os.path.join(output_dir, 'tables', 'daily_returns.csv')
+    returns.to_csv(daily_returns_file, encoding='utf-8-sig')
+    print(f"✓ 日收益率数据已保存: tables/daily_returns.csv")
+
+    # 2. 创建各资产收益率统计表格
+    asset_returns_summary = []
+
+    for asset in returns.columns:
+        asset_ret = returns[asset]
+
+        # 计算各项统计
+        mean_return = asset_ret.mean() * 100
+        std_return = asset_ret.std() * 100
+        max_return = asset_ret.max() * 100
+        min_return = asset_ret.min() * 100
+        positive_days = (asset_ret > 0).sum()
+        total_days = len(asset_ret)
+        win_rate = positive_days / total_days * 100
+
+        # 计算累计收益率
+        cumulative_ret = (1 + asset_ret).cumprod() - 1
+        total_cumulative = cumulative_ret.iloc[-1] * 100
+
+        asset_returns_summary.append({
+            '资产': asset,
+            '日均收益率%': mean_return,
+            '日收益率标准差%': std_return,
+            '单日最大收益%': max_return,
+            '单日最大亏损%': min_return,
+            '累计收益率%': total_cumulative,
+            '正收益天数': positive_days,
+            '总天数': total_days,
+            '胜率%': win_rate
+        })
+
+    # 添加投资组合收益率统计
+    port_mean_return = portfolio_returns.mean() * 100
+    port_std_return = portfolio_returns.std() * 100
+    port_max_return = portfolio_returns.max() * 100
+    port_min_return = portfolio_returns.min() * 100
+    port_positive_days = (portfolio_returns > 0).sum()
+    port_total_days = len(portfolio_returns)
+    port_win_rate = port_positive_days / port_total_days * 100
+    port_total_cumulative = cumulative_returns.iloc[-1] * 100
+
+    asset_returns_summary.append({
+        '资产': '投资组合',
+        '日均收益率%': port_mean_return,
+        '日收益率标准差%': port_std_return,
+        '单日最大收益%': port_max_return,
+        '单日最大亏损%': port_min_return,
+        '累计收益率%': port_total_cumulative,
+        '正收益天数': port_positive_days,
+        '总天数': port_total_days,
+        '胜率%': port_win_rate
+    })
+
+    # 保存收益率汇总
+    returns_summary_df = pd.DataFrame(asset_returns_summary)
+    returns_summary_file = os.path.join(output_dir, 'tables', 'returns_summary.csv')
+    returns_summary_df.to_csv(returns_summary_file, encoding='utf-8-sig', index=False)
+    print(f"✓ 收益率汇总数据已保存: tables/returns_summary.csv")
+
+    # 3. 保存月度收益率数据
+    all_assets = list(returns.columns) + ['投资组合']
+    all_returns = pd.concat([returns, portfolio_returns.rename('投资组合')], axis=1)
+
+    # 计算月度收益率
+    monthly_returns = all_returns.resample('M').apply(lambda x: (1 + x).prod() - 1)
+    monthly_returns_file = os.path.join(output_dir, 'tables', 'monthly_returns.csv')
+    monthly_returns.to_csv(monthly_returns_file, encoding='utf-8-sig')
+    print(f"✓ 月度收益率数据已保存: tables/monthly_returns.csv")
+
+    # 4. 保存年度收益率数据
+    yearly_returns = all_returns.resample('Y').apply(lambda x: (1 + x).prod() - 1)
+    yearly_returns_file = os.path.join(output_dir, 'tables', 'yearly_returns.csv')
+    yearly_returns.to_csv(yearly_returns_file, encoding='utf-8-sig')
+    print(f"✓ 年度收益率数据已保存: tables/yearly_returns.csv")
+
+    return returns_summary_df
+
+
+def save_portfolio_summary_table(prices, asset_names, weights, portfolio_returns, cumulative_returns, metrics, returns,
+                                 output_dir):
+    """
+    保存投资组合总结表格到CSV文件
+    """
+    print("生成投资组合总结表格...")
+
+    stock_prices = prices[asset_names].copy()
+
+    # 1. 投资组合配置表格
+    config_data = []
+    total_investment = 10000  # 假设初始投资10000元
+
+    for i, asset in enumerate(asset_names):
+        weight_pct = weights[i] * 100
+        allocation = total_investment * weights[i]
+
+        # 计算该资产的表现
+        asset_ret = returns[asset]
+        asset_cumulative = (1 + asset_ret).cumprod() - 1
+        asset_total_return = asset_cumulative.iloc[-1] * 100
+        asset_annual_vol = asset_ret.std() * np.sqrt(252) * 100
+
+        config_data.append({
+            '资产': asset,
+            '权重%': weight_pct,
+            '配置金额(假设¥10,000)': allocation,
+            '累计收益%': asset_total_return,
+            '年化波动率%': asset_annual_vol
+        })
+
+    config_df = pd.DataFrame(config_data)
+    config_file = os.path.join(output_dir, 'tables', 'portfolio_configuration.csv')
+    config_df.to_csv(config_file, encoding='utf-8-sig', index=False)
+    print(f"✓ 投资组合配置表格已保存: tables/portfolio_configuration.csv")
+
+    # 2. 投资组合绩效指标表格
+    performance_data = [
+        {'指标类别': '时间信息', '指标名称': '分析期间', '数值': f"{metrics['起始日期']} 至 {metrics['结束日期']}"},
+        {'指标类别': '时间信息', '指标名称': '总交易日数', '数值': f"{metrics['交易日数']} 天"},
+        {'指标类别': '时间信息', '指标名称': '分析周期', '数值': f"{metrics['总年数']:.2f} 年"},
+
+        {'指标类别': '收益指标', '指标名称': '累计收益率', '数值': f"{metrics['累计收益率'] * 100:.2f}%"},
+        {'指标类别': '收益指标', '指标名称': '年化收益率', '数值': f"{metrics['年化收益率'] * 100:.2f}%"},
+        {'指标类别': '收益指标', '指标名称': '日均收益率', '数值': f"{metrics['平均日收益'] * 100:.4f}%"},
+        {'指标类别': '收益指标', '指标名称': '正收益天数', '数值': f"{metrics['正收益天数']} 天"},
+        {'指标类别': '收益指标', '指标名称': '胜率', '数值': f"{metrics['胜率'] * 100:.1f}%"},
+
+        {'指标类别': '风险指标', '指标名称': '年化波动率', '数值': f"{metrics['年化波动率'] * 100:.2f}%"},
+        {'指标类别': '风险指标', '指标名称': '最大回撤', '数值': f"{metrics['最大回撤'] * 100:.2f}%"},
+        {'指标类别': '风险指标', '指标名称': '年化下行风险', '数值': f"{metrics['年化下行风险'] * 100:.2f}%"},
+        {'指标类别': '风险指标', '指标名称': '日收益率标准差', '数值': f"{metrics['收益标准差'] * 100:.4f}%"},
+
+        {'指标类别': '风险调整后收益', '指标名称': '夏普比率', '数值': f"{metrics['夏普比率']:.3f}"},
+        {'指标类别': '风险调整后收益', '指标名称': '索提诺比率', '数值': f"{metrics['索提诺比率']:.3f}"}
+    ]
+
+    performance_df = pd.DataFrame(performance_data)
+    performance_file = os.path.join(output_dir, 'tables', 'portfolio_performance.csv')
+    performance_df.to_csv(performance_file, encoding='utf-8-sig', index=False)
+    print(f"✓ 投资组合绩效指标表格已保存: tables/portfolio_performance.csv")
+
+    # 3. 回撤分析表格
+    cumulative_value = (1 + portfolio_returns).cumprod()
+    running_max = cumulative_value.expanding().max()
+    drawdown_series = (cumulative_value - running_max) / running_max
+
+    # 找到回撤超过5%的事件
+    drawdown_events = []
+    in_drawdown = False
+    current_drawdown = {'start': None, 'trough': None, 'end': None, 'max_dd': 0}
+
+    for date, dd in drawdown_series.items():
+        if dd < -0.05:  # 超过5%的回撤
+            if not in_drawdown:
+                in_drawdown = True
+                current_drawdown['start'] = date
+                current_drawdown['max_dd'] = dd
+                current_drawdown['trough'] = date
+            else:
+                if dd < current_drawdown['max_dd']:
+                    current_drawdown['max_dd'] = dd
+                    current_drawdown['trough'] = date
+        else:
+            if in_drawdown:
+                in_drawdown = False
+                current_drawdown['end'] = date
+
+                # 计算回撤持续天数
+                duration = (current_drawdown['end'] - current_drawdown['start']).days
+
+                drawdown_events.append({
+                    '开始日期': current_drawdown['start'].strftime('%Y-%m-%d'),
+                    '最低点日期': current_drawdown['trough'].strftime('%Y-%m-%d'),
+                    '结束日期': current_drawdown['end'].strftime('%Y-%m-%d'),
+                    '持续天数': duration,
+                    '最大回撤%': current_drawdown['max_dd'] * 100
+                })
+
+                current_drawdown = {'start': None, 'trough': None, 'end': None, 'max_dd': 0}
+
+    if drawdown_events:
+        drawdown_df = pd.DataFrame(drawdown_events)
+        drawdown_file = os.path.join(output_dir, 'tables', 'drawdown_analysis.csv')
+        drawdown_df.to_csv(drawdown_file, encoding='utf-8-sig', index=False)
+        print(f"✓ 回撤分析表格已保存: tables/drawdown_analysis.csv")
+    else:
+        print("⚠ 无超过5%的重大回撤事件")
+
+    # 4. 相关性分析表格
+    correlation_matrix = returns.corr()
+    correlation_file = os.path.join(output_dir, 'tables', 'correlation_matrix.csv')
+    correlation_matrix.to_csv(correlation_file, encoding='utf-8-sig')
+    print(f"✓ 相关性矩阵表格已保存: tables/correlation_matrix.csv")
+
+    # 5. 投资建议总结
+    summary_points = []
+
+    # 基于收益率评价
+    if metrics['年化收益率'] > 0.10:
+        summary_points.append('优秀收益：年化收益率超过10%')
+    elif metrics['年化收益率'] > 0.05:
+        summary_points.append('良好收益：年化收益率在5%-10%之间')
+    elif metrics['年化收益率'] > 0:
+        summary_points.append('正收益：年化收益率为正')
+    else:
+        summary_points.append('负收益：年化收益率为负')
+
+    # 基于夏普比率评价
+    if metrics['夏普比率'] > 1.0:
+        summary_points.append('优秀风险调整收益：夏普比率超过1.0')
+    elif metrics['夏普比率'] > 0.5:
+        summary_points.append('良好风险调整收益：夏普比率在0.5-1.0之间')
+    elif metrics['夏普比率'] > 0:
+        summary_points.append('正风险调整收益：夏普比率为正')
+    else:
+        summary_points.append('负风险调整收益：夏普比率为负')
+
+    # 基于最大回撤评价
+    if abs(metrics['最大回撤']) < 0.10:
+        summary_points.append('风险控制优秀：最大回撤小于10%')
+    elif abs(metrics['最大回撤']) < 0.20:
+        summary_points.append('风险控制良好：最大回撤在10%-20%之间')
+    elif abs(metrics['最大回撤']) < 0.30:
+        summary_points.append('风险较高：最大回撤在20%-30%之间')
+    else:
+        summary_points.append('风险很高：最大回撤超过30%')
+
+    # 基于胜率评价
+    if metrics['胜率'] > 0.55:
+        summary_points.append('高胜率：超过55%的交易日期获得正收益')
+    elif metrics['胜率'] > 0.45:
+        summary_points.append('适中胜率：在45%-55%之间')
+    else:
+        summary_points.append('低胜率：少于45%的交易日期获得正收益')
+
+    # 保存投资建议
+    summary_df = pd.DataFrame({'投资建议': summary_points})
+    summary_file = os.path.join(output_dir, 'tables', 'investment_recommendations.csv')
+    summary_df.to_csv(summary_file, encoding='utf-8-sig', index=False)
+    print(f"✓ 投资建议表格已保存: tables/investment_recommendations.csv")
+
+    return config_df, performance_df
+
+
+def save_charts(prices, asset_names, portfolio_returns, cumulative_returns, drawdown, metrics, weights, output_dir):
+    """
+    保存分析图表为PNG图片
+    """
+    print("生成分析图表...")
+
+    stock_prices = prices[asset_names].copy()
+
+    # 图表1: 价格走势对比图
+    plt.figure(figsize=(14, 8))
+    colors = ['#1f77b4', '#ff7f0e']
+
+    for i, asset in enumerate(asset_names):
+        normalized = stock_prices[asset] / stock_prices[asset].iloc[0] * 100
+        plt.plot(stock_prices.index, normalized, label=f"{asset} ({weights[i] * 100:.0f}%)",
+                 color=colors[i], linewidth=2.5)
+
+    plt.title(f'归一化价格走势 (基准=100)\n{metrics["起始日期"]} 至 {metrics["结束日期"]}',
+              fontsize=14, fontweight='bold')
+    plt.ylabel('价格指数', fontsize=12)
+    plt.xlabel('日期', fontsize=12)
+    plt.legend(fontsize=11)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    price_chart_file = os.path.join(output_dir, 'charts', '01_price_trend.png')
+    plt.savefig(price_chart_file, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"✓ 价格走势图已保存: charts/01_price_trend.png")
+
+    # 图表2: 投资组合累计收益
+    plt.figure(figsize=(14, 8))
+    plt.plot(cumulative_returns.index, cumulative_returns * 100,
+             color='#2e8b57', linewidth=3)
+    plt.fill_between(cumulative_returns.index, 0, cumulative_returns * 100,
+                     where=cumulative_returns >= 0, color='#2e8b57', alpha=0.3)
+
+    plt.title(f'投资组合累计收益率\n年化收益率: {metrics["年化收益率"] * 100:.2f}%',
+              fontsize=14, fontweight='bold')
+    plt.ylabel('累计收益 (%)', fontsize=12)
+    plt.xlabel('日期', fontsize=12)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    cumulative_chart_file = os.path.join(output_dir, 'charts', '02_cumulative_returns.png')
+    plt.savefig(cumulative_chart_file, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"✓ 累计收益图已保存: charts/02_cumulative_returns.png")
+
+    # 图表3: 回撤分析
+    plt.figure(figsize=(14, 8))
+    plt.fill_between(drawdown.index, drawdown * 100, 0,
+                     where=drawdown < 0, color='#ff6b6b', alpha=0.6)
+    plt.plot(drawdown.index, drawdown * 100, color='#c44d58', linewidth=2)
+
+    plt.title(f'投资组合回撤分析\n最大回撤: {metrics["最大回撤"] * 100:.2f}%',
+              fontsize=14, fontweight='bold')
+    plt.ylabel('回撤幅度 (%)', fontsize=12)
+    plt.xlabel('日期', fontsize=12)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    drawdown_chart_file = os.path.join(output_dir, 'charts', '03_drawdown_analysis.png')
+    plt.savefig(drawdown_chart_file, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"✓ 回撤分析图已保存: charts/03_drawdown_analysis.png")
+
+    # 图表4: 日收益率分布
+    plt.figure(figsize=(14, 8))
+    returns_pct = portfolio_returns * 100
+    n_bins = min(50, len(returns_pct) // 5)
+
+    plt.hist(returns_pct, bins=n_bins, alpha=0.7, color='#4ecdc4',
+             edgecolor='black', density=True)
+    plt.axvline(x=returns_pct.mean(), color='red', linestyle='--',
+                linewidth=2, label=f'均值: {returns_pct.mean():.2f}%')
+
+    # 添加正态分布曲线
+    try:
+        from scipy.stats import norm
+        mu, std = returns_pct.mean(), returns_pct.std()
+        x = np.linspace(returns_pct.min(), returns_pct.max(), 100)
+        p = norm.pdf(x, mu, std)
+        plt.plot(x, p, 'k', linewidth=2, label='正态分布')
+    except:
+        pass
+
+    plt.title('日收益率分布', fontsize=14, fontweight='bold')
+    plt.xlabel('日收益率 (%)', fontsize=12)
+    plt.ylabel('概率密度', fontsize=12)
+    plt.legend(fontsize=11)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    distribution_chart_file = os.path.join(output_dir, 'charts', '04_returns_distribution.png')
+    plt.savefig(distribution_chart_file, dpi=300, bbox_inches='tight')
+    plt.close()
+    print(f"✓ 收益率分布图已保存: charts/04_returns_distribution.png")
+
+    # 图表5: 月度收益率热力图（如果数据足够）
+    if len(portfolio_returns) >= 60:
+        plt.figure(figsize=(14, 10))
+        monthly_returns = portfolio_returns.resample('M').apply(lambda x: (1 + x).prod() - 1)
+
+        if len(monthly_returns) > 0:
+            years = sorted(set(monthly_returns.index.year))
+            monthly_matrix = pd.DataFrame(index=years, columns=range(1, 13))
+
+            for year in years:
+                for month in range(1, 13):
+                    month_data = monthly_returns[(monthly_returns.index.year == year) &
+                                                 (monthly_returns.index.month == month)]
+                    if not month_data.empty:
+                        monthly_matrix.loc[year, month] = month_data.iloc[0]
+
+            monthly_matrix_numeric = monthly_matrix.astype(float) * 100
+            plt.imshow(monthly_matrix_numeric.values, cmap='RdYlGn',
+                       aspect='auto', alpha=0.9, vmin=-15, vmax=15)
+
+            # 添加数值标签
+            for i in range(len(years)):
+                for j in range(12):
+                    value = monthly_matrix_numeric.iloc[i, j]
+                    if not np.isnan(value):
+                        color = 'black' if abs(value) < 8 else 'white'
+                        plt.text(j, i, f'{value:.1f}%', ha='center', va='center',
+                                 fontsize=9, color=color, fontweight='bold')
+
+            plt.xticks(range(12), ['1月', '2月', '3月', '4月', '5月', '6月',
+                                   '7月', '8月', '9月', '10月', '11月', '12月'],
+                       rotation=45)
+            plt.yticks(range(len(years)), years)
+            plt.colorbar(label='月收益率 (%)')
+            plt.title('月度收益率热力图', fontsize=14, fontweight='bold')
+            plt.xlabel('月份', fontsize=12)
+            plt.ylabel('年份', fontsize=12)
+            plt.tight_layout()
+
+            heatmap_file = os.path.join(output_dir, 'charts', '05_monthly_returns_heatmap.png')
+            plt.savefig(heatmap_file, dpi=300, bbox_inches='tight')
+            plt.close()
+            print(f"✓ 月度收益率热力图已保存: charts/05_monthly_returns_heatmap.png")
+
+    # 图表6: 年度收益率条形图
+    if len(portfolio_returns) >= 252:
+        plt.figure(figsize=(14, 8))
+        yearly_returns = portfolio_returns.resample('Y').apply(lambda x: (1 + x).prod() - 1)
+
+        if len(yearly_returns) > 0:
+            yearly_returns.index = yearly_returns.index.year
+            colors_bar = ['#2ecc71' if x >= 0 else '#e74c3c' for x in yearly_returns]
+
+            bars = plt.bar(yearly_returns.index.astype(str), yearly_returns.values * 100,
+                           color=colors_bar, edgecolor='black', alpha=0.8)
+
+            # 添加数值标签
+            for i, v in enumerate(yearly_returns.values):
+                plt.text(i, v * 100 + (0.5 if v >= 0 else -1), f'{v * 100:.1f}%',
+                         ha='center', va='bottom' if v >= 0 else 'top',
+                         fontsize=10, fontweight='bold')
+
+            plt.title('年度收益率', fontsize=14, fontweight='bold')
+            plt.ylabel('收益率 (%)', fontsize=12)
+            plt.xlabel('年份', fontsize=12)
+            plt.grid(True, alpha=0.3, axis='y')
+            plt.tight_layout()
+
+            yearly_file = os.path.join(output_dir, 'charts', '06_yearly_returns.png')
+            plt.savefig(yearly_file, dpi=300, bbox_inches='tight')
+            plt.close()
+            print(f"✓ 年度收益率图已保存: charts/06_yearly_returns.png")
+
+    # 图表7: 滚动收益分析
+    if len(portfolio_returns) >= 60:
+        plt.figure(figsize=(14, 8))
+        window_size = min(60, len(portfolio_returns) // 2)
+        rolling_return = portfolio_returns.rolling(window=window_size).mean() * 100
+
+        plt.plot(rolling_return.index, rolling_return, color='#4169e1', linewidth=2)
+        plt.axhline(y=portfolio_returns.mean() * 100, color='red',
+                    linestyle='--', linewidth=1.5, label=f'平均: {portfolio_returns.mean() * 100:.2f}%')
+
+        plt.title(f'滚动{window_size}日平均收益率', fontsize=14, fontweight='bold')
+        plt.ylabel('日收益率 (%)', fontsize=12)
+        plt.xlabel('日期', fontsize=12)
+        plt.legend(fontsize=11)
+        plt.grid(True, alpha=0.3)
+        plt.tight_layout()
+
+        rolling_file = os.path.join(output_dir, 'charts', '07_rolling_returns.png')
+        plt.savefig(rolling_file, dpi=300, bbox_inches='tight')
+        plt.close()
+        print(f"✓ 滚动收益图已保存: charts/07_rolling_returns.png")
+
+    print(f"✓ 所有图表已保存至 {output_dir}/charts/ 目录")
+
+
+def save_raw_data(prices, asset_names, returns, portfolio_returns, cumulative_returns, drawdown, output_dir):
+    """
+    保存原始数据文件
+    """
+    print("保存原始数据文件...")
+
+    stock_prices = prices[asset_names].copy()
+
+    # 保存原始价格数据
+    raw_prices_file = os.path.join(output_dir, 'data', 'raw_prices.csv')
+    stock_prices.to_csv(raw_prices_file, encoding='utf-8-sig')
+    print(f"✓ 原始价格数据已保存: data/raw_prices.csv")
+
+    # 保存汇率数据
+    exchange_rate_file = os.path.join(output_dir, 'data', 'exchange_rate.csv')
+    prices['汇率_USD_CNY'].to_csv(exchange_rate_file, encoding='utf-8-sig')
+    print(f"✓ 汇率数据已保存: data/exchange_rate.csv")
+
+    # 保存收益率数据
+    raw_returns_file = os.path.join(output_dir, 'data', 'raw_returns.csv')
+    returns.to_csv(raw_returns_file, encoding='utf-8-sig')
+    print(f"✓ 原始收益率数据已保存: data/raw_returns.csv")
+
+    # 保存投资组合收益率数据
+    portfolio_data = pd.DataFrame({
+        '日收益率': portfolio_returns,
+        '累计收益率': cumulative_returns,
+        '回撤': drawdown
+    })
+    portfolio_file = os.path.join(output_dir, 'data', 'portfolio_performance.csv')
+    portfolio_data.to_csv(portfolio_file, encoding='utf-8-sig')
+    print(f"✓ 投资组合表现数据已保存: data/portfolio_performance.csv")
+
+    # 创建README文件
+    readme_content = f"""# 投资组合分析结果
+
+## 分析概要
+- 分析资产: A股515450 + 美股QLD
+- 分析期间: {stock_prices.index[0].date()} 至 {stock_prices.index[-1].date()}
+- 总交易日数: {len(stock_prices)}
+- 生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+
+## 目录结构
+{output_dir}/
+├── tables/           # 各种分析表格（CSV格式）
+│   ├── stock_prices_detailed.csv      # 详细价格数据
+│   ├── stock_prices_summary.csv       # 价格汇总统计
+│   ├── daily_returns.csv              # 日收益率数据
+│   ├── returns_summary.csv            # 收益率汇总统计
+│   ├── monthly_returns.csv            # 月度收益率
+│   ├── yearly_returns.csv             # 年度收益率
+│   ├── portfolio_configuration.csv    # 投资组合配置
+│   ├── portfolio_performance.csv      # 绩效指标
+│   ├── drawdown_analysis.csv          # 回撤分析
+│   ├── correlation_matrix.csv         # 相关性矩阵
+│   ├── investment_recommendations.csv # 投资建议
+│   ├── investment_simulation_details.csv # 投资模拟详情
+│   ├── investment_simulation_summary.csv # 投资模拟汇总
+│   └── investment_asset_details.csv   # 各资产投资明细
+├── charts/           # 分析图表（PNG格式）
+│   ├── 01_price_trend.png             # 价格走势图
+│   ├── 02_cumulative_returns.png      # 累计收益图
+│   ├── 03_drawdown_analysis.png       # 回撤分析图
+│   ├── 04_returns_distribution.png    # 收益率分布图
+│   ├── 05_monthly_returns_heatmap.png # 月度收益率热力图
+│   ├── 06_yearly_returns.png          # 年度收益率图
+│   ├── 07_rolling_returns.png         # 滚动收益图
+│   ├── 08_investment_growth.png       # 资产增长曲线
+│   ├── 09_investment_returns.png      # 投资累计收益率
+│   └── 10_investment_progress.png     # 投资进度图
+└── data/             # 原始数据文件
+    ├── raw_prices.csv                 # 原始价格数据
+    ├── raw_returns.csv                # 原始收益率数据
+    ├── portfolio_performance.csv      # 投资组合表现数据
+    └── exchange_rate.csv              # 汇率数据
+
+## 使用说明
+1. 所有表格均为CSV格式，可用Excel、Python pandas等工具打开
+2. 所有图表均为PNG格式，分辨率为300dpi
+3. 原始数据可用于进一步分析
+4. 投资建议基于历史数据分析，仅供参考
+
+## 注意事项
+- 本分析基于历史数据，不代表未来表现
+- 投资有风险，决策需谨慎
+- 建议结合其他分析工具和个人风险偏好进行投资决策
+"""
+
+    readme_file = os.path.join(output_dir, 'README.txt')
+    with open(readme_file, 'w', encoding='utf-8') as f:
+        f.write(readme_content)
+    print(f"✓ README文件已生成: README.txt")
+
+
+def main():
+    """
+    主程序
+    """
+    print("=" * 80)
+    print("A股515450与美股QLD投资组合分析系统")
+    print("=" * 80)
+
+    # 创建输出目录
+    output_dir = create_output_directory()
+
+    try:
+        # 1. 获取股票数据
+        print("\n步骤1: 获取股票数据")
+        prices = get_stock_data()
+
+        # 2. 获取用户输入的权重
+        print("\n步骤2: 配置投资组合权重")
+        weights, asset_names = get_user_weights(list(prices.columns))
+
+        # 3. 获取投资计划
+        print("\n步骤3: 配置投资计划")
+        investment_plan = get_investment_plan()
+
+        # 4. 计算投资组合表现
+        print("\n步骤4: 计算投资组合表现")
+        portfolio_returns, cumulative_returns, drawdown, metrics, returns = calculate_portfolio_performance(
+            prices, weights, asset_names
+        )
+
+        # 5. 计算投资模拟
+        print("\n步骤5: 计算投资模拟")
+        simulation_results = calculate_investment_simulation(prices, weights, asset_names, investment_plan)
+
+        # 6. 保存所有表格到CSV文件
+        print("\n步骤6: 生成分析表格（CSV格式）")
+        save_stock_price_table(prices, asset_names, output_dir)
+        save_returns_table(prices, asset_names, returns, portfolio_returns, cumulative_returns, output_dir)
+        save_portfolio_summary_table(prices, asset_names, weights, portfolio_returns, cumulative_returns, metrics,
+                                     returns,
+                                     output_dir)
+        save_investment_simulation_results(simulation_results, output_dir)
+
+        # 7. 保存原始数据
+        save_raw_data(prices, asset_names, returns, portfolio_returns, cumulative_returns, drawdown, output_dir)
+
+        # 8. 生成并保存所有图表为PNG文件
+        print("\n步骤7: 生成分析图表（PNG格式）")
+        save_charts(prices, asset_names, portfolio_returns, cumulative_returns, drawdown, metrics, weights, output_dir)
+        create_investment_charts(simulation_results, output_dir)
+
+        # 9. 在终端显示关键结果摘要
+        print("\n" + "=" * 80)
+        print("投资组合关键结果摘要")
+        print("=" * 80)
+        print(f"分析期间: {metrics['起始日期']} 至 {metrics['结束日期']}")
+        print(f"投资周期: {metrics['总年数']:.2f} 年")
+        print(f"年化收益率: {metrics['年化收益率'] * 100:.2f}%")
+        print(f"累计收益率: {metrics['累计收益率'] * 100:.2f}%")
+        print(f"年化波动率: {metrics['年化波动率'] * 100:.2f}%")
+        print(f"最大回撤: {metrics['最大回撤'] * 100:.2f}%")
+        print(f"夏普比率: {metrics['夏普比率']:.3f}")
+        print(f"索提诺比率: {metrics['索提诺比率']:.3f}")
+        print(f"胜率: {metrics['胜率'] * 100:.1f}%")
+
+        # 10. 显示投资模拟结果摘要
+        summary = simulation_results['summary']
+        print("\n" + "=" * 80)
+        print("投资模拟结果摘要")
+        print("=" * 80)
+        print(f"初始本金: {summary['初始本金_元']:.2f} 元")
+        print(f"每月定投: {summary['每月定投_元']:.2f} 元")
+        print(f"总投资额: {summary['总投资额_元']:.2f} 元")
+        print(f"最终资产总值: {summary['最终资产总值_元']:.2f} 元")
+        print(f"总收益: {summary['总收益_元']:.2f} 元")
+        print(f"总收益率: {summary['总收益率_%']:.2f}%")
+        print(f"年化收益率: {summary['年化收益率_%']:.2f}%")
+        print(f"投资期间: {summary['投资起始日期']} 至 {summary['投资结束日期']}")
+        print(f"投资天数: {summary['投资天数']} 天 ({summary['投资年数']:.2f} 年)")
+        print(f"投资笔数: {summary['现金投资笔数']} 笔")
+        print(f"投资组合理论增长倍数: {summary['投资组合增长倍数']:.4f}")
+
+        print("\n" + "=" * 80)
+        print(f"分析完成! 所有结果已保存至: {output_dir}/")
+        print("=" * 80)
+        print("包含以下内容:")
+        print("✓ 表格文件 (CSV格式): tables/ 目录")
+        print("✓ 图表文件 (PNG格式): charts/ 目录")
+        print("✓ 原始数据: data/ 目录")
+        print("✓ 说明文件: README.txt")
+        print("=" * 80)
+
+    except KeyboardInterrupt:
+        print("\n\n程序被用户中断")
+        return 1
+    except Exception as e:
+        print(f"\n错误: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        print("程序退出")
+        return 1
+
+    return 0
+
+
+if __name__ == "__main__":
+    # 检查必要库是否安装
+    required_libs = ['akshare', 'yfinance', 'pandas', 'numpy', 'matplotlib']
+
+    missing_libs = []
+    for lib in required_libs:
+        try:
+            __import__(lib)
+        except ImportError:
+            missing_libs.append(lib)
+
+    if missing_libs:
+        print(f"错误: 缺少必要的库: {', '.join(missing_libs)}")
+        print("请运行以下命令安装:")
+        print(f"pip install {' '.join(missing_libs)}")
+        if 'akshare' in missing_libs:
+            print("\n注意: AKShare可能需要额外配置，如果安装失败可以尝试:")
+            print("pip install akshare --upgrade")
+        exit(1)
+
+    # 运行主程序
+    print("程序启动 - A股515450与美股QLD投资组合分析")
+    exit_code = main()
+    exit(exit_code)
\ No newline at end of file
diff --git a/portfolio_analysis_20251205_190343/README.txt b/portfolio_analysis_20251205_190343/README.txt
new file mode 100644
index 0000000..5cdc10c
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/README.txt
@@ -0,0 +1,52 @@
+# 投资组合分析结果
+
+## 分析概要
+- 分析资产: A股515450 + 美股QQQ
+- 分析期间: 2020-12-07 至 2025-12-05
+- 总交易日数: 1300
+- 生成时间: 2025-12-05 19:04:16
+
+## 目录结构
+portfolio_analysis_20251205_190343/
+├── tables/           # 各种分析表格（CSV格式）
+│   ├── stock_prices_detailed.csv      # 详细价格数据
+│   ├── stock_prices_summary.csv       # 价格汇总统计
+│   ├── daily_returns.csv              # 日收益率数据
+│   ├── returns_summary.csv            # 收益率汇总统计
+│   ├── monthly_returns.csv            # 月度收益率
+│   ├── yearly_returns.csv             # 年度收益率
+│   ├── portfolio_configuration.csv    # 投资组合配置
+│   ├── portfolio_performance.csv      # 绩效指标
+│   ├── drawdown_analysis.csv          # 回撤分析
+│   ├── correlation_matrix.csv         # 相关性矩阵
+│   ├── investment_recommendations.csv # 投资建议
+│   ├── investment_simulation_details.csv # 投资模拟详情
+│   ├── investment_simulation_summary.csv # 投资模拟汇总
+│   └── investment_asset_details.csv   # 各资产投资明细
+├── charts/           # 分析图表（PNG格式）
+│   ├── 01_price_trend.png             # 价格走势图
+│   ├── 02_cumulative_returns.png      # 累计收益图
+│   ├── 03_drawdown_analysis.png       # 回撤分析图
+│   ├── 04_returns_distribution.png    # 收益率分布图
+│   ├── 05_monthly_returns_heatmap.png # 月度收益率热力图
+│   ├── 06_yearly_returns.png          # 年度收益率图
+│   ├── 07_rolling_returns.png         # 滚动收益图
+│   ├── 08_investment_growth.png       # 资产增长曲线
+│   ├── 09_investment_returns.png      # 投资累计收益率
+│   └── 10_investment_progress.png     # 投资进度图
+└── data/             # 原始数据文件
+    ├── raw_prices.csv                 # 原始价格数据
+    ├── raw_returns.csv                # 原始收益率数据
+    ├── portfolio_performance.csv      # 投资组合表现数据
+    └── exchange_rate.csv              # 汇率数据
+
+## 使用说明
+1. 所有表格均为CSV格式，可用Excel、Python pandas等工具打开
+2. 所有图表均为PNG格式，分辨率为300dpi
+3. 原始数据可用于进一步分析
+4. 投资建议基于历史数据分析，仅供参考
+
+## 注意事项
+- 本分析基于历史数据，不代表未来表现
+- 投资有风险，决策需谨慎
+- 建议结合其他分析工具和个人风险偏好进行投资决策
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diff --git a/portfolio_analysis_20251205_190343/data/portfolio_performance.csv b/portfolio_analysis_20251205_190343/data/portfolio_performance.csv
new file mode 100644
index 0000000..d08cc22
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/data/portfolio_performance.csv
@@ -0,0 +1,1300 @@
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diff --git a/portfolio_analysis_20251205_190343/data/raw_prices.csv b/portfolio_analysis_20251205_190343/data/raw_prices.csv
new file mode 100644
index 0000000..40f0cae
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/data/raw_prices.csv
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diff --git a/portfolio_analysis_20251205_190343/data/raw_returns.csv b/portfolio_analysis_20251205_190343/data/raw_returns.csv
new file mode 100644
index 0000000..f18e722
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/data/raw_returns.csv
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diff --git a/portfolio_analysis_20251205_190343/tables/daily_returns.csv b/portfolio_analysis_20251205_190343/tables/daily_returns.csv
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diff --git a/portfolio_analysis_20251205_190343/tables/drawdown_analysis.csv b/portfolio_analysis_20251205_190343/tables/drawdown_analysis.csv
new file mode 100644
index 0000000..2218017
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/drawdown_analysis.csv
@@ -0,0 +1,19 @@
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diff --git a/portfolio_analysis_20251205_190343/tables/investment_asset_details.csv b/portfolio_analysis_20251205_190343/tables/investment_asset_details.csv
new file mode 100644
index 0000000..af478db
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/investment_asset_details.csv
@@ -0,0 +1,3 @@
+资产名称,配置权重,投资金额(元),最终价值(元),资产收益(元),资产收益率(%)
+A股_515450,40.0%,28000.00,57301.41,29301.41,104.65
+美股_QQQ,60.0%,42000.00,95030.40,53030.40,126.26
diff --git a/portfolio_analysis_20251205_190343/tables/investment_recommendations.csv b/portfolio_analysis_20251205_190343/tables/investment_recommendations.csv
new file mode 100644
index 0000000..4a423f6
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/investment_recommendations.csv
@@ -0,0 +1,5 @@
+投资建议
+优秀收益：年化收益率超过10%
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+风险较高：最大回撤在20%-30%之间
+适中胜率：在45%-55%之间
diff --git a/portfolio_analysis_20251205_190343/tables/investment_simulation_details.csv b/portfolio_analysis_20251205_190343/tables/investment_simulation_details.csv
new file mode 100644
index 0000000..6422348
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/investment_simulation_details.csv
@@ -0,0 +1,1301 @@
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diff --git a/portfolio_analysis_20251205_190343/tables/investment_simulation_summary.csv b/portfolio_analysis_20251205_190343/tables/investment_simulation_summary.csv
new file mode 100644
index 0000000..466ed7c
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/investment_simulation_summary.csv
@@ -0,0 +1,14 @@
+指标,数值
+初始本金,10000.00 元
+每月定投金额,1000.00 元
+总投资金额,70000.00 元
+最终资产总值,125322.47 元
+总收益,55322.47 元
+总收益率,79.03%
+投资天数,1824 天
+投资年数,4.99 年
+年化收益率,12.37%
+投资起始日期,2020-12-07
+投资结束日期,2025-12-05
+投资组合增长倍数,2.2626
+现金投资笔数,61 笔
diff --git a/portfolio_analysis_20251205_190343/tables/monthly_returns.csv b/portfolio_analysis_20251205_190343/tables/monthly_returns.csv
new file mode 100644
index 0000000..a6e5586
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/monthly_returns.csv
@@ -0,0 +1,62 @@
+Date,A股_515450,美股_QQQ,投资组合
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diff --git a/portfolio_analysis_20251205_190343/tables/portfolio_configuration.csv b/portfolio_analysis_20251205_190343/tables/portfolio_configuration.csv
new file mode 100644
index 0000000..6a3366f
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/portfolio_configuration.csv
@@ -0,0 +1,3 @@
+资产,权重%,"配置金额(假设¥10,000)",累计收益%,年化波动率%
+A股_515450,40.0,4000.0,104.6478873239447,18.182899641321402
+美股_QQQ,60.0,6000.0,109.03466068947787,22.271976902809236
diff --git a/portfolio_analysis_20251205_190343/tables/portfolio_performance.csv b/portfolio_analysis_20251205_190343/tables/portfolio_performance.csv
new file mode 100644
index 0000000..b57128d
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/portfolio_performance.csv
@@ -0,0 +1,15 @@
+指标类别,指标名称,数值
+时间信息,分析期间,2020-12-07 至 2025-12-05
+时间信息,总交易日数,1299 天
+时间信息,分析周期,4.99 年
+收益指标,累计收益率,118.29%
+收益指标,年化收益率,16.92%
+收益指标,日均收益率,0.0646%
+收益指标,正收益天数,705 天
+收益指标,胜率,54.3%
+风险指标,年化波动率,15.12%
+风险指标,最大回撤,-26.00%
+风险指标,年化下行风险,10.40%
+风险指标,日收益率标准差,0.9523%
+风险调整后收益,夏普比率,0.987
+风险调整后收益,索提诺比率,1.435
diff --git a/portfolio_analysis_20251205_190343/tables/returns_summary.csv b/portfolio_analysis_20251205_190343/tables/returns_summary.csv
new file mode 100644
index 0000000..31d97bd
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/returns_summary.csv
@@ -0,0 +1,4 @@
+资产,日均收益率%,日收益率标准差%,单日最大收益%,单日最大亏损%,累计收益率%,正收益天数,总天数,胜率%
+A股_515450,0.06170392771528309,1.1454150134519516,7.131537242472263,-8.41121495327103,104.6478873239447,603,1299,46.4203233256351
+美股_QQQ,0.06659646500778976,1.4030026688239954,12.00308181135814,-6.210886967050778,109.03466068947787,690,1299,53.11778290993071
+投资组合,0.0646394500907871,0.9523130222588321,7.083066607230623,-3.7265321802304663,118.28638572226033,705,1299,54.27251732101617
diff --git a/portfolio_analysis_20251205_190343/tables/stock_prices_detailed.csv b/portfolio_analysis_20251205_190343/tables/stock_prices_detailed.csv
new file mode 100644
index 0000000..40f0cae
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/stock_prices_detailed.csv
@@ -0,0 +1,1301 @@
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diff --git a/portfolio_analysis_20251205_190343/tables/stock_prices_summary.csv b/portfolio_analysis_20251205_190343/tables/stock_prices_summary.csv
new file mode 100644
index 0000000..743fefa
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/stock_prices_summary.csv
@@ -0,0 +1,3 @@
+资产名称,起始价格,结束价格,总收益率%,年化收益率%,最高价格,最低价格,平均价格,价格标准差,数据点数,起始日期,结束日期
+A股_515450,0.71,1.453,104.64788732394368,15.419232450949071,1.49,0.672,1.0504576923076923,0.23247297006631246,1300,2020-12-07,2025-12-05
+美股_QQQ,298.00799560546875,622.9400024414062,109.03466068947805,15.910468706517111,635.77001953125,255.5943145751953,395.3660239703839,95.85359595479295,1300,2020-12-07,2025-12-05
diff --git a/portfolio_analysis_20251205_190343/tables/yearly_returns.csv b/portfolio_analysis_20251205_190343/tables/yearly_returns.csv
new file mode 100644
index 0000000..023773d
--- /dev/null
+++ b/portfolio_analysis_20251205_190343/tables/yearly_returns.csv
@@ -0,0 +1,7 @@
+Date,A股_515450,美股_QQQ,投资组合
+2020-12-31,-0.028169014084507782,0.022973589895306956,0.0026693478985437835
+2021-12-31,0.3057971014492762,0.2741976397937589,0.299740624879248
+2022-12-31,-0.053274139844617485,-0.32577015425318634,-0.212995524281959
+2023-12-31,0.20750293083235638,0.5485560368358247,0.41042314567907723
+2024-12-31,0.3524271844660216,0.2557826334648212,0.30427402439152185
+2025-12-31,0.0430725053840626,0.2231161881697863,0.15695573826719333
diff --git a/portfolio_analysis_20251205_190933/README.txt b/portfolio_analysis_20251205_190933/README.txt
new file mode 100644
index 0000000..2291311
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/README.txt
@@ -0,0 +1,52 @@
+# 投资组合分析结果
+
+## 分析概要
+- 分析资产: A股515450 + 美股QLD
+- 分析期间: 2020-12-07 至 2025-12-05
+- 总交易日数: 1300
+- 生成时间: 2025-12-05 19:10:15
+
+## 目录结构
+portfolio_analysis_20251205_190933/
+├── tables/           # 各种分析表格（CSV格式）
+│   ├── stock_prices_detailed.csv      # 详细价格数据
+│   ├── stock_prices_summary.csv       # 价格汇总统计
+│   ├── daily_returns.csv              # 日收益率数据
+│   ├── returns_summary.csv            # 收益率汇总统计
+│   ├── monthly_returns.csv            # 月度收益率
+│   ├── yearly_returns.csv             # 年度收益率
+│   ├── portfolio_configuration.csv    # 投资组合配置
+│   ├── portfolio_performance.csv      # 绩效指标
+│   ├── drawdown_analysis.csv          # 回撤分析
+│   ├── correlation_matrix.csv         # 相关性矩阵
+│   ├── investment_recommendations.csv # 投资建议
+│   ├── investment_simulation_details.csv # 投资模拟详情
+│   ├── investment_simulation_summary.csv # 投资模拟汇总
+│   └── investment_asset_details.csv   # 各资产投资明细
+├── charts/           # 分析图表（PNG格式）
+│   ├── 01_price_trend.png             # 价格走势图
+│   ├── 02_cumulative_returns.png      # 累计收益图
+│   ├── 03_drawdown_analysis.png       # 回撤分析图
+│   ├── 04_returns_distribution.png    # 收益率分布图
+│   ├── 05_monthly_returns_heatmap.png # 月度收益率热力图
+│   ├── 06_yearly_returns.png          # 年度收益率图
+│   ├── 07_rolling_returns.png         # 滚动收益图
+│   ├── 08_investment_growth.png       # 资产增长曲线
+│   ├── 09_investment_returns.png      # 投资累计收益率
+│   └── 10_investment_progress.png     # 投资进度图
+└── data/             # 原始数据文件
+    ├── raw_prices.csv                 # 原始价格数据
+    ├── raw_returns.csv                # 原始收益率数据
+    ├── portfolio_performance.csv      # 投资组合表现数据
+    └── exchange_rate.csv              # 汇率数据
+
+## 使用说明
+1. 所有表格均为CSV格式，可用Excel、Python pandas等工具打开
+2. 所有图表均为PNG格式，分辨率为300dpi
+3. 原始数据可用于进一步分析
+4. 投资建议基于历史数据分析，仅供参考
+
+## 注意事项
+- 本分析基于历史数据，不代表未来表现
+- 投资有风险，决策需谨慎
+- 建议结合其他分析工具和个人风险偏好进行投资决策
diff --git a/portfolio_analysis_20251205_190933/charts/01_price_trend.png b/portfolio_analysis_20251205_190933/charts/01_price_trend.png
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diff --git a/portfolio_analysis_20251205_190933/charts/03_drawdown_analysis.png b/portfolio_analysis_20251205_190933/charts/03_drawdown_analysis.png
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diff --git a/portfolio_analysis_20251205_190933/charts/05_monthly_returns_heatmap.png b/portfolio_analysis_20251205_190933/charts/05_monthly_returns_heatmap.png
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diff --git a/portfolio_analysis_20251205_190933/charts/06_yearly_returns.png b/portfolio_analysis_20251205_190933/charts/06_yearly_returns.png
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diff --git a/portfolio_analysis_20251205_190933/charts/07_rolling_returns.png b/portfolio_analysis_20251205_190933/charts/07_rolling_returns.png
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diff --git a/portfolio_analysis_20251205_190933/charts/08_investment_growth.png b/portfolio_analysis_20251205_190933/charts/08_investment_growth.png
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index 0000000..c23894d
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diff --git a/portfolio_analysis_20251205_190933/charts/09_investment_returns.png b/portfolio_analysis_20251205_190933/charts/09_investment_returns.png
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diff --git a/portfolio_analysis_20251205_190933/charts/10_investment_progress.png b/portfolio_analysis_20251205_190933/charts/10_investment_progress.png
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index 0000000..d058587
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diff --git a/portfolio_analysis_20251205_190933/data/exchange_rate.csv b/portfolio_analysis_20251205_190933/data/exchange_rate.csv
new file mode 100644
index 0000000..2eac3fe
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/data/exchange_rate.csv
@@ -0,0 +1,1301 @@
+Date,汇率_USD_CNY
+2020-12-07,653.62
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diff --git a/portfolio_analysis_20251205_190933/data/portfolio_performance.csv b/portfolio_analysis_20251205_190933/data/portfolio_performance.csv
new file mode 100644
index 0000000..16f6338
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/data/portfolio_performance.csv
@@ -0,0 +1,1300 @@
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diff --git a/portfolio_analysis_20251205_190933/data/raw_prices.csv b/portfolio_analysis_20251205_190933/data/raw_prices.csv
new file mode 100644
index 0000000..cb07d72
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/data/raw_prices.csv
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diff --git a/portfolio_analysis_20251205_190933/data/raw_returns.csv b/portfolio_analysis_20251205_190933/data/raw_returns.csv
new file mode 100644
index 0000000..5f54f8d
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/data/raw_returns.csv
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diff --git a/portfolio_analysis_20251205_190933/tables/correlation_matrix.csv b/portfolio_analysis_20251205_190933/tables/correlation_matrix.csv
new file mode 100644
index 0000000..ab63526
--- /dev/null
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diff --git a/portfolio_analysis_20251205_190933/tables/daily_returns.csv b/portfolio_analysis_20251205_190933/tables/daily_returns.csv
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index 0000000..5f54f8d
--- /dev/null
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diff --git a/portfolio_analysis_20251205_190933/tables/drawdown_analysis.csv b/portfolio_analysis_20251205_190933/tables/drawdown_analysis.csv
new file mode 100644
index 0000000..a13e4e2
--- /dev/null
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@@ -0,0 +1,31 @@
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diff --git a/portfolio_analysis_20251205_190933/tables/investment_asset_details.csv b/portfolio_analysis_20251205_190933/tables/investment_asset_details.csv
new file mode 100644
index 0000000..d613b6d
--- /dev/null
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@@ -0,0 +1,3 @@
+资产名称,配置权重,投资金额(元),最终价值(元),资产收益(元),资产收益率(%)
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+美股_QLD,60.0%,42000.00,121068.72,79068.72,188.26
diff --git a/portfolio_analysis_20251205_190933/tables/investment_recommendations.csv b/portfolio_analysis_20251205_190933/tables/investment_recommendations.csv
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index 0000000..780ade6
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/investment_recommendations.csv
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+投资建议
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+风险很高：最大回撤超过30%
+适中胜率：在45%-55%之间
diff --git a/portfolio_analysis_20251205_190933/tables/investment_simulation_details.csv b/portfolio_analysis_20251205_190933/tables/investment_simulation_details.csv
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index 0000000..d603829
--- /dev/null
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diff --git a/portfolio_analysis_20251205_190933/tables/investment_simulation_summary.csv b/portfolio_analysis_20251205_190933/tables/investment_simulation_summary.csv
new file mode 100644
index 0000000..49542c8
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/investment_simulation_summary.csv
@@ -0,0 +1,14 @@
+指标,数值
+初始本金,10000.00 元
+每月定投金额,1000.00 元
+总投资金额,70000.00 元
+最终资产总值,160627.06 元
+总收益,90627.06 元
+总收益率,129.47%
+投资天数,1824 天
+投资年数,4.99 年
+年化收益率,18.10%
+投资起始日期,2020-12-07
+投资结束日期,2025-12-05
+投资组合增长倍数,2.8826
+现金投资笔数,61 笔
diff --git a/portfolio_analysis_20251205_190933/tables/monthly_returns.csv b/portfolio_analysis_20251205_190933/tables/monthly_returns.csv
new file mode 100644
index 0000000..f5b814c
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/monthly_returns.csv
@@ -0,0 +1,62 @@
+Date,A股_515450,美股_QLD,投资组合
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diff --git a/portfolio_analysis_20251205_190933/tables/portfolio_configuration.csv b/portfolio_analysis_20251205_190933/tables/portfolio_configuration.csv
new file mode 100644
index 0000000..6a3cd40
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/portfolio_configuration.csv
@@ -0,0 +1,3 @@
+资产,权重%,"配置金额(假设¥10,000)",累计收益%,年化波动率%
+A股_515450,40.0,4000.0,104.6478873239447,18.182899641321402
+美股_QLD,60.0,6000.0,166.3101204053575,44.53233309483803
diff --git a/portfolio_analysis_20251205_190933/tables/portfolio_performance.csv b/portfolio_analysis_20251205_190933/tables/portfolio_performance.csv
new file mode 100644
index 0000000..8f0bffc
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/portfolio_performance.csv
@@ -0,0 +1,15 @@
+指标类别,指标名称,数值
+时间信息,分析期间,2020-12-07 至 2025-12-05
+时间信息,总交易日数,1299 天
+时间信息,分析周期,4.99 年
+收益指标,累计收益率,176.89%
+收益指标,年化收益率,22.62%
+收益指标,日均收益率,0.0935%
+收益指标,正收益天数,699 天
+收益指标,胜率,53.8%
+风险指标,年化波动率,27.59%
+风险指标,最大回撤,-44.99%
+风险指标,年化下行风险,19.43%
+风险指标,日收益率标准差,1.7379%
+风险调整后收益,夏普比率,0.748
+风险调整后收益,索提诺比率,1.061
diff --git a/portfolio_analysis_20251205_190933/tables/returns_summary.csv b/portfolio_analysis_20251205_190933/tables/returns_summary.csv
new file mode 100644
index 0000000..9ab83dc
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/returns_summary.csv
@@ -0,0 +1,4 @@
+资产,日均收益率%,日收益率标准差%,单日最大收益%,单日最大亏损%,累计收益率%,正收益天数,总天数,胜率%
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+美股_QLD,0.11469872180478634,2.8052733016769706,23.4929328172222,-12.212730032427366,166.3101204053575,686,1299,52.80985373364126
+投资组合,0.09350080416898503,1.7379167439877563,13.976977210749059,-7.327638019456419,176.8948938144213,699,1299,53.81062355658198
diff --git a/portfolio_analysis_20251205_190933/tables/stock_prices_detailed.csv b/portfolio_analysis_20251205_190933/tables/stock_prices_detailed.csv
new file mode 100644
index 0000000..cb07d72
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/stock_prices_detailed.csv
@@ -0,0 +1,1301 @@
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diff --git a/portfolio_analysis_20251205_190933/tables/stock_prices_summary.csv b/portfolio_analysis_20251205_190933/tables/stock_prices_summary.csv
new file mode 100644
index 0000000..1c5e490
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/stock_prices_summary.csv
@@ -0,0 +1,3 @@
+资产名称,起始价格,结束价格,总收益率%,年化收益率%,最高价格,最低价格,平均价格,价格标准差,数据点数,起始日期,结束日期
+A股_515450,0.71,1.453,104.64788732394368,15.419232450949071,1.49,0.672,1.0504576923076923,0.23247297006631246,1300,2020-12-07,2025-12-05
+美股_QLD,27.26896095275879,72.62000274658203,166.31012040535816,21.669712491977087,76.38500213623047,16.587970733642578,38.406993022331825,13.585959300291872,1300,2020-12-07,2025-12-05
diff --git a/portfolio_analysis_20251205_190933/tables/yearly_returns.csv b/portfolio_analysis_20251205_190933/tables/yearly_returns.csv
new file mode 100644
index 0000000..679e2f6
--- /dev/null
+++ b/portfolio_analysis_20251205_190933/tables/yearly_returns.csv
@@ -0,0 +1,7 @@
+Date,A股_515450,美股_QLD,投资组合
+2020-12-31,-0.028169014084507782,0.04360832351935784,0.015108043137775917
+2021-12-31,0.3057971014492762,0.5466943981092007,0.47861179916538865
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