def sample_531_2():
"""
5.3.1_2 绘制股票的收益，及收益波动情况
:return:
"""
tsla_df_copy = tsla_df.copy()
# 投资回报
tsla_df_copy['return'] = np.log(tsla_df['close'] / tsla_df['close'].shift(1))
# 移动收益标准差
tsla_df_copy['mov_std'] = pd_rolling_std(tsla_df_copy['return'],
window=20,
center=False) * np.sqrt(20)
# 加权移动收益标准差，与移动收益标准差基本相同，只不过根据时间权重计算std
tsla_df_copy['std_ewm'] = pd_ewm_std(tsla_df_copy['return'], span=20,
min_periods=20,
adjust=True) * np.sqrt(20)
tsla_df_copy[['close', 'mov_std', 'std_ewm', 'return']].plot(subplots=True, grid=True)
plt.show()
def sample_531_2():
"""
5.3.1_2 绘制股票的收益，及收益波动情况
:return:
"""
tsla_df_copy = tsla_df.copy()
# 投资回报
tsla_df_copy['return'] = np.log(tsla_df['close'] / tsla_df['close'].shift(1))
# 移动收益标准差
tsla_df_copy['mov_std'] = pd_rolling_std(tsla_df_copy['return'],
window=20,
center=False) * np.sqrt(20)
# 加权移动收益标准差，与移动收益标准差基本相同，只不过根据时间权重计算std
tsla_df_copy['std_ewm'] = pd_ewm_std(tsla_df_copy['return'], span=20,
min_periods=20,
adjust=True) * np.sqrt(20)
tsla_df_copy[['close', 'mov_std', 'std_ewm', 'return']].plot(subplots=True, grid=True)
plt.show()