def sample_531_1():
"""
5.3.1_1 绘制股票的收益，及收益波动情况 demo list
:return:
"""
# 示例序列
demo_list = np.array([2, 4, 16, 20])
# 以三天为周期计算波动
demo_window = 3
# pd.rolling_std * np.sqrt
print('pd.rolling_std(demo_list, window=demo_window, center=False) * np.sqrt(demo_window):\n',
pd_rolling_std(demo_list, window=demo_window, center=False) * np.sqrt(demo_window))
print('pd.Series([2, 4, 16]).std() * np.sqrt(demo_window):', pd.Series([2, 4, 16]).std() * np.sqrt(demo_window))
print('pd.Series([4, 16, 20]).std() * np.sqrt(demo_window):', pd.Series([4, 16, 20]).std() * np.sqrt(demo_window))
print('np.sqrt(pd.Series([2, 4, 16]).var() * demo_window):', np.sqrt(pd.Series([2, 4, 16]).var() * demo_window))
def sample_531_1():
"""
5.3.1_1 绘制股票的收益，及收益波动情况 demo list
:return:
"""
# 示例序列
demo_list = np.array([2, 4, 16, 20])
# 以三天为周期计算波动
demo_window = 3
# pd.rolling_std * np.sqrt
print('pd.rolling_std(demo_list, window=demo_window, center=False) * np.sqrt(demo_window):\n',
pd_rolling_std(demo_list, window=demo_window, center=False) * np.sqrt(demo_window))
print('pd.Series([2, 4, 16]).std() * np.sqrt(demo_window):', pd.Series([2, 4, 16]).std() * np.sqrt(demo_window))
print('pd.Series([4, 16, 20]).std() * np.sqrt(demo_window):', pd.Series([4, 16, 20]).std() * np.sqrt(demo_window))
print('np.sqrt(pd.Series([2, 4, 16]).var() * demo_window):', np.sqrt(pd.Series([2, 4, 16]).var() * demo_window))
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()