def calc_calendar_distribution(df_signal):
daily_signal = df_signal.sum(axis=1)
daily_signal = daily_signal.fillna(0).astype(int)
idx = daily_signal.index.values
month = jutil.date_to_month(idx)
year = jutil.date_to_year(idx)
monthly_signal = daily_signal.groupby(by=month).sum()
yearly_signal = daily_signal.groupby(by=year).sum()
monthly_signal = pd.DataFrame(monthly_signal, columns=['Time'])
yearly_signal = pd.DataFrame(yearly_signal, columns=['Time'])
monthly_signal.index.name = 'Month'
yearly_signal.index.name = 'Year'
return daily_signal, monthly_signal, yearly_signal
def calc_calendar_distribution(df_signal):
daily_signal = df_signal.sum(axis=1)
daily_signal = daily_signal.fillna(0).astype(int)
idx = daily_signal.index.values
month = jutil.date_to_month(idx)
year = jutil.date_to_year(idx)
monthly_signal = daily_signal.groupby(by=month).sum()
yearly_signal = daily_signal.groupby(by=year).sum()
monthly_signal = pd.DataFrame(monthly_signal, columns=['Time'])
yearly_signal = pd.DataFrame(yearly_signal, columns=['Time'])
monthly_signal.index.name = 'Month'
yearly_signal.index.name = 'Year'
return daily_signal, monthly_signal, yearly_signal
def get_stats(self):
df_return = self.returns.copy()
idx = df_return.index
df_return.loc[:,
'daily_active'] = df_return['strat'] - df_return['bench']
df_return.loc[:, 'month'] = jutil.date_to_month(idx)
df_return.loc[:, 'year'] = jutil.date_to_year(idx)
def calc_cum(df):
return np.prod(df.add(1.0)) - 1.0
stats_monthly = df_return.groupby('month')['daily_active'].apply(
calc_cum)
stats_yearly = df_return.groupby('year')['daily_active'].apply(
calc_cum)
'''
plt.figure(figsize=(16, 10))
sns.distplot(df_return['daily_active'], bins=np.arange(-5e-2, 5e-2, 1e-3), kde=False)
plt.savefig('a.png')
plt.close()
'''
print