Cat Tom
1783 lines
72 KiB
Python
1783 lines
72 KiB
Python
import yfinance as yf
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import akshare as ak
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib
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from datetime import datetime, timedelta
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import os
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import warnings
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warnings.filterwarnings('ignore')
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# 设置中文字体
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matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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matplotlib.rcParams['axes.unicode_minus'] = False
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def create_output_directory():
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"""创建输出目录"""
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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output_dir = f"portfolio_analysis_{timestamp}"
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# 创建主目录
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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# 创建子目录
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subdirs = ['tables', 'charts', 'data']
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for subdir in subdirs:
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subdir_path = os.path.join(output_dir, subdir)
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if not os.path.exists(subdir_path):
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os.makedirs(subdir_path)
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print(f"输出目录已创建: {output_dir}/")
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return output_dir
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def get_a_stock_data(symbol='515450', years=5):
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"""
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使用AKShare获取A股515450近五年的数据
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"""
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print(f"正在通过AKShare获取A股 {symbol} 近{years}年数据...")
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# 计算5年前的日期
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end_date = datetime.now()
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start_date = end_date - timedelta(days=years * 365)
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# 格式化日期
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start_str = start_date.strftime('%Y%m%d')
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end_str = end_date.strftime('%Y%m%d')
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try:
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print("尝试使用ETF基金接口获取A股数据...")
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# 方法1：尝试使用ETF基金接口
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try:
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df = ak.fund_etf_hist_em(symbol=symbol, period="daily",
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start_date=start_str, end_date=end_str,
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adjust="qfq")
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print("使用ETF基金接口成功")
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except Exception as e1:
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print(f"ETF基金接口失败: {e1}")
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# 方法2：尝试使用股票接口
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print("尝试使用股票接口...")
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try:
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# 尝试不同的后缀
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for suffix in ['.SZ', '.SS', '']:
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try:
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stock_symbol = f"{symbol}{suffix}" if suffix else symbol
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df = ak.stock_zh_a_hist(symbol=stock_symbol, period="daily",
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start_date=start_str, end_date=end_str,
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adjust="qfq")
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print(f"使用股票接口成功: {stock_symbol}")
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break
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except:
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continue
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except Exception as e2:
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print(f"股票接口失败: {e2}")
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raise ValueError(f"无法通过AKShare获取A股{symbol}数据")
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if df.empty:
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raise ValueError(f"未能获取到A股 {symbol} 的数据")
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print(f"获取到 {len(df)} 行数据")
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# 重命名列
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column_mapping = {
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'日期': 'Date',
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'时间': 'Date',
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'收盘': 'Close',
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'收盘价': 'Close',
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'price': 'Close',
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'最新价': 'Close'
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}
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for old_col, new_col in column_mapping.items():
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if old_col in df.columns:
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df = df.rename(columns={old_col: new_col})
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# 确保Date列是datetime类型
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if 'Date' in df.columns:
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df['Date'] = pd.to_datetime(df['Date'])
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df = df.set_index('Date')
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else:
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# 如果没有Date列，使用索引
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df.index = pd.to_datetime(df.index)
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# 按日期排序
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df = df.sort_index()
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# 提取收盘价
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close_price = None
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for col in ['Close', '收盘', '收盘价', 'price', '最新价']:
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if col in df.columns:
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close_price = df[col]
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print(f"使用 '{col}' 列作为收盘价")
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break
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if close_price is None and len(df.columns) > 0:
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print("警告: 未找到标准收盘价列，尝试使用第一列数值列")
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for col in df.columns:
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if pd.api.types.is_numeric_dtype(df[col]):
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close_price = df[col]
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print(f"使用 '{col}' 列作为收盘价")
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break
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if close_price is None:
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raise ValueError("无法提取收盘价数据")
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# 转换为数值类型并处理缺失值
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close_price = pd.to_numeric(close_price, errors='coerce')
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close_price = close_price.ffill().bfill()
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# 移除时区信息
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close_price.index = close_price.index.tz_localize(None)
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print(f"A股 {symbol} 数据获取成功!")
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print(f"数据时间范围: {close_price.index[0].date()} 到 {close_price.index[-1].date()}")
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print(f"总交易日数: {len(close_price)}")
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return close_price
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except Exception as e:
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print(f"通过AKShare获取A股 {symbol} 数据时出错: {e}")
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raise
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def get_us_stock_data(symbol='QLD', years=5):
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"""
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使用yfinance获取美股数据（使用QLD）
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"""
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print(f"正在通过yfinance获取美股 {symbol} 近{years}年数据...")
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# 计算5年前的日期
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end_date = datetime.now()
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start_date = end_date - timedelta(days=years * 365)
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# 格式化日期为字符串
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start_str = start_date.strftime('%Y-%m-%d')
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end_str = end_date.strftime('%Y-%m-%d')
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print(f"时间范围: {start_str} 到 {end_str}")
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try:
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# 创建Ticker对象
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ticker = yf.Ticker(symbol)
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# 获取历史数据
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stock_data = ticker.history(start=start_str, end=end_str)
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if stock_data.empty:
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print(f"警告: {symbol} 数据为空，尝试获取最大历史数据...")
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stock_data = ticker.history(period="max")
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if stock_data.empty:
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raise ValueError(f"未能获取到美股 {symbol} 的数据")
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# 截取最近5年的数据
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cutoff_date = end_date - timedelta(days=years * 365)
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stock_data = stock_data[stock_data.index >= pd.Timestamp(cutoff_date)]
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print("使用最大历史数据截取成功")
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print(f"美股 {symbol} 数据下载成功!")
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print(
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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
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print(f"总交易日数: {len(stock_data)}")
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# 提取收盘价
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close_price = None
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# 优先使用调整后收盘价
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if 'Adj Close' in stock_data.columns:
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close_price = stock_data['Adj Close']
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print("使用 'Adj Close' 列作为收盘价")
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elif 'Close' in stock_data.columns:
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close_price = stock_data['Close']
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print("使用 'Close' 列作为收盘价")
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else:
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# 尝试寻找包含'Close'的列
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for col in stock_data.columns:
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if 'close' in str(col).lower():
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close_price = stock_data[col]
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print(f"使用 '{col}' 列作为收盘价")
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break
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if close_price is None:
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print("警告: 未找到标准收盘价列，尝试使用第一列数值列")
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for col in stock_data.columns:
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if pd.api.types.is_numeric_dtype(stock_data[col]):
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close_price = stock_data[col]
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print(f"使用 '{col}' 列作为收盘价")
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break
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if close_price is None:
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raise ValueError("无法提取收盘价数据")
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# 转换为数值类型并处理缺失值
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close_price = pd.to_numeric(close_price, errors='coerce')
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close_price = close_price.ffill().bfill()
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# 移除时区信息
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close_price.index = close_price.index.tz_localize(None)
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return close_price
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except Exception as e:
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print(f"通过yfinance获取美股 {symbol} 数据时出错: {e}")
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raise
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def get_exchange_rate_data(years=5):
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"""
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获取人民币兑美元汇率数据（无参数版本）
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"""
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print("正在获取人民币兑美元汇率数据...")
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# 计算日期范围
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end_date = datetime.now()
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start_date = end_date - timedelta(days=years * 365)
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try:
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# 直接调用函数，无任何参数
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df = ak.currency_boc_safe()
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if df.empty:
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raise ValueError("未能获取到汇率数据")
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print(f"获取到 {len(df)} 条汇率数据")
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print(f"数据列名: {df.columns.tolist()}")
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print(f"数据示例:\n{df.head()}")
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# 检查数据列名，根据实际列名重命名
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# 注意：列名可能是中文的
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column_mapping = {}
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# 查找日期列
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date_columns = ['日期', 'Date', 'date', 'time', '交易日', '交易日期']
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for col in date_columns:
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if col in df.columns:
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column_mapping[col] = 'Date'
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break
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# 查找汇率列（美元兑人民币中间价）
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rate_columns = ['美元', 'USD', '中间价', '现汇卖出价', '现汇买入价', 'price', 'rate', '汇率']
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for col in rate_columns:
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if col in df.columns:
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column_mapping[col] = 'Exchange_Rate'
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break
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# 如果找到了需要重命名的列，执行重命名
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if column_mapping:
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df = df.rename(columns=column_mapping)
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print(f"已将列名映射为: {column_mapping}")
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# 确保有Date列
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if 'Date' not in df.columns and len(df.columns) > 0:
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# 使用第一列作为日期
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first_col = df.columns[0]
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df = df.rename(columns={first_col: 'Date'})
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print(f"将第一列 '{first_col}' 作为Date列")
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# 确保有Exchange_Rate列
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if 'Exchange_Rate' not in df.columns and len(df.columns) > 1:
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# 使用第二列作为汇率
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second_col = df.columns[1]
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df = df.rename(columns={second_col: 'Exchange_Rate'})
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print(f"将第二列 '{second_col}' 作为Exchange_Rate列")
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# 转换为datetime类型
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df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
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df = df.dropna(subset=['Date'])
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df = df.set_index('Date')
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df = df.sort_index()
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# 提取汇率数据
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if 'Exchange_Rate' in df.columns:
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exchange_rate = df['Exchange_Rate']
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else:
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# 如果没有Exchange_Rate列，尝试使用第一个数值列
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for col in df.columns:
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if pd.api.types.is_numeric_dtype(df[col]):
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exchange_rate = df[col]
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print(f"使用数值列 '{col}' 作为汇率数据")
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break
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else:
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raise ValueError("找不到汇率数据列")
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# 转换为数值类型
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exchange_rate = pd.to_numeric(exchange_rate, errors='coerce')
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exchange_rate = exchange_rate.ffill().bfill()
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# 筛选指定日期范围
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exchange_rate = exchange_rate[(exchange_rate.index >= start_date) &
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(exchange_rate.index <= end_date)]
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if len(exchange_rate) == 0:
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print("警告: 指定日期范围内无汇率数据，使用全部数据")
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exchange_rate = exchange_rate
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print(f"汇率数据获取成功!")
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print(f"数据时间范围: {exchange_rate.index[0].date()} 到 {exchange_rate.index[-1].date()}")
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print(f"可用数据点数: {len(exchange_rate)}")
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print(f"最新汇率: 1美元 = {exchange_rate.iloc[-1]:.4f} 人民币")
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# 如果需要，可以插值生成每日数据
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if len(exchange_rate) < years * 365 * 0.5: # 如果数据点少于预期的一半
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print("数据点较少，生成完整日期序列...")
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full_index = pd.date_range(start=start_date, end=end_date, freq='D')
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exchange_rate = exchange_rate.reindex(full_index)
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exchange_rate = exchange_rate.ffill().bfill()
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print(f"插值后数据点数: {len(exchange_rate)}")
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return exchange_rate
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except Exception as e:
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print(f"获取汇率数据时出错: {e}")
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import traceback
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traceback.print_exc()
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print("将使用固定汇率7.1进行估算")
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# 创建完整的日期序列
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all_dates = pd.date_range(start=start_date, end=end_date, freq='D')
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exchange_rate = pd.Series(7.1, index=all_dates, name='Exchange_Rate')
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return exchange_rate
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def get_stock_data():
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"""
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获取A股515450和美股QLD近五年的收盘价数据，以及汇率数据
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"""
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print("=" * 80)
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print("获取近五年A股515450与美股QLD数据")
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print("=" * 80)
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all_data = {}
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try:
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# 获取A股515450数据
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print("\n1. 获取A股515450数据...")
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a_stock_price = get_a_stock_data('515450', years=5)
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a_stock_price.name = 'A股_515450'
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all_data['A股_515450'] = a_stock_price
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# 获取美股QLD数据
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print("\n2. 获取美股QLD数据...")
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us_stock_price = get_us_stock_data('QLD', years=5)
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us_stock_price.name = '美股_QLD'
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all_data['美股_QLD'] = us_stock_price
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# 获取汇率数据
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print("\n3. 获取汇率数据...")
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exchange_rate = get_exchange_rate_data(years=5)
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all_data['汇率_USD_CNY'] = exchange_rate
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# 创建空的DataFrame
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combined_data = pd.DataFrame()
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# 逐个添加数据
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for name, data in all_data.items():
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temp_df = pd.DataFrame({name: data})
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if combined_data.empty:
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combined_data = temp_df
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else:
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combined_data = combined_data.merge(temp_df,
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left_index=True,
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right_index=True,
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how='outer')
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# 按日期排序
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combined_data = combined_data.sort_index()
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# 处理缺失值 - 前向填充和后向填充
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combined_data = combined_data.ffill().bfill()
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combined_data = combined_data.dropna()
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except Exception as e:
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print(f"\n数据获取失败: {e}")
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raise
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if combined_data.empty:
|
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raise ValueError("数据合并后为空，请检查数据源")
|
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# 计算实际的时间跨度
|
||
start_date = combined_data.index[0]
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end_date = combined_data.index[-1]
|
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actual_days = (end_date - start_date).days
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actual_years = actual_days / 365.25
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print(f"\n数据获取完成!")
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print(f"总数据行数: {len(combined_data)}")
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print(f"实际时间范围: {start_date.date()} 到 {end_date.date()}")
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print(f"实际时间跨度: {actual_days} 天 ({actual_years:.2f} 年)")
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print(f"可用数据列: {list(combined_data.columns)}")
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return combined_data
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def get_user_weights(asset_names):
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"""
|
||
获取用户输入的投资权重
|
||
"""
|
||
print("\n" + "=" * 60)
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||
print("投资组合权重配置")
|
||
print("=" * 60)
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# 过滤掉汇率列
|
||
filtered_assets = [asset for asset in asset_names if '汇率' not in asset]
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print(f"可用资产: {', '.join(filtered_assets)}")
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||
print("请输入每个资产的权重百分比（总和应为100%）")
|
||
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||
weights = {}
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||
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while True:
|
||
try:
|
||
total_weight = 0
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||
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||
for i, asset in enumerate(filtered_assets):
|
||
if i == 0:
|
||
prompt = f"请输入 {asset} 的权重百分比 (0-100): "
|
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else:
|
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remaining = 100 - total_weight
|
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prompt = f"请输入 {asset} 的权重百分比 (剩余 {remaining}%): "
|
||
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||
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) |