首先，你需要安装pandas和matplotlib库，如果还没安装的话可以使用pip安装： ```bash pip install pandas matplotlib ``` 然后，你可以按照以下步骤操作： 1. 导入所需的库： ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt ``` 2. 加载数据到DataFrame `data`，假设数据已经是一个包含日期、股票代码和close价格的CSV文件： ```python data = pd.read_csv('stock_data.csv') # 替换为你实际的数据文件路径 data['date'] = pd.to_datetime(data['date']) # 如果date列不是datetime类型，需要转换 data.set_index('date', inplace=True) # 设置日期为索引 ``` 3. 计算简单的移动平均线(SMA)： ```python # 定义函数计算SMA def calculate_sma(prices, window): return prices.rolling(window=window).mean() # 分别计算5日和20日SMA sma_5 = calculate_sma(data['close'], 5) sma_20 = calculate_sma(data['close'], 20) ``` 4. 绘制图表： ```python fig, ax1 = plt.subplots(figsize=(12,6)) color = 'tab:red' ax1.set_xlabel('Date') ax1.set_ylabel('Close Price', color=color) ax1.plot(data.index, data['close'], label='Close Price', color=color) ax1.tick_params(axis='y', labelcolor=color) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis color = 'tab:blue' ax2.set_ylabel('SMA (5 Day)', color=color) # we already handled the x-label with ax1 ax2.plot(data.index, sma_5, label='SMA 5', color=color) ax2.plot(data.index, sma_20, label='SMA 20', linestyle='--', color=color) ax2.tick_params(axis='y', labelcolor=color) fig.tight_layout() # otherwise the right y-label is slightly clipped plt.title('Stock Prices and Moving Averages') ax1.legend(loc='upper left') ax2.legend(loc='upper right') plt.show() ```