def regression(self, X, years, monthEndDate='lastMonthEnd'):
""""OLS regression the ReturnSeries self is the dependent variable
Args:
X(ReturnFrame, ReturnSeries): independent variables in ReturnFrame for 1 or more variables, ReturnSeries for 1
monthEndDate(str, pd.TimeStamp): str format 'yyyy-mm-dd'
years(int): sets start date 1st day of the month # years from end date
Returns:
tuple(params(DataFrame), r squared(float)): params contains coefficients, tvalues, pvalues"""
# set start and end date
end = dtf.get_month_end(self, monthEndDate)
start = dtf.relative_start(end, years)
# prepare variable arrays
y = self[start:end]
if isinstance(X, ReturnSeries):
X = ReturnFrame(X.loc[start:end]).copy()
elif isinstance(X, ReturnFrame):
X = X.loc[start:end, :].copy()
X['Alpha'] = 1 # add intercept (see statmodels docs)
# fit model and prepare parameter Dataframe to return
model = sm.OLS(y, X)
result = model.fit()
params = pd.concat([result.params, result.tvalues, result.pvalues],
axis=1).rename(columns={
0: "params",
1: "tvalues",
2: "pvalues"
})
return params, result.rsquared
def compounded_series(self, monthStartDate, monthEndDate='lastMonthEnd'):
# returns a compounded return series starting from monthStartDate till monthEndDate
end = dtf.get_month_end(self, monthEndDate)
start = dtf.ts_date(monthStartDate) - pd.offsets.BMonthBegin(n=0)
period = self[start:end]
compoundedSrs = pd.Series(index=period.index)
for i in range(len(period)):
if i == 0:
compoundedSrs.iloc[i] = period.iloc[0]
else:
compoundedSrs.iloc[i] = (compoundedSrs.iloc[i - 1] +
1) * (period.iloc[i] + 1) - 1
return compoundedSrs
def annualize(self, years, monthEndDate='lastMonthEnd'):
""""Annualizes return over a period going back # years from monthEndDate
Args:
years(int):period history in years which will be used for annualizing
monthEndDate(pd.Timestamp, str): default "lastMonthEnd" selects the last day month end business day in the
data. Dates can be given in strings as "yyyy-mm-dd" data will select month end of month in date string same
as for Timestamps.
Returns:
tuple( return, sigma): returns a tuple with the annualized return and standard deviation respectively
"""
end = dtf.get_month_end(self, monthEndDate)
start = dtf.relative_start(end, years)
period = self[start:end]
r_annualized = (_chainlink(period) + 1)**(251 / len(period)) - 1
sigma_annualized = period.std() * (251**(0.5))
return (r_annualized, sigma_annualized
) # period removed from the return
def compounded_return(self, monthStartDate, monthEndDate='lastMonthEnd'):
# returns a single compounded return over period monthStartDate to monthEndDate
end = dtf.get_month_end(self, monthEndDate)
start = dtf.ts_date(monthStartDate) - pd.offsets.BMonthBegin(n=0)
return _chainlink(self[start:end])
def month_return(self, monthEndDate='lastMonthEnd'):
end = dtf.get_month_end(self, monthEndDate)
start = end - pd.offsets.BMonthBegin(n=1)
return _chainlink(self[start:end])
# date determines the end date of the period over which the calculations are performed
date = 'lastMonthEnd'
# each int in yearList determines the period timespan going back from date
yearList = [1, 3, 5]
# variables to determine sheet and file output names
sheetName = 'factsheet'
fileName = 'factsheet.xlsx'
# ===== no more declarations code executes below ======
# get data
df = dataModule.get_data()
date4xl = dtf.get_month_end(df, date=date).date()
# add column for y variable in regression
df.loc[:, y] = df.loc[:, fund] - df.loc[:, rf_rate]
# Create output dataframes
dfS = get_riskstats(df=df, fund=fund, benchmark=benchmark, rf_rate=rf_rate, yearList=yearList, date=date)
dfR = get_returns(df=df, fund=fund, benchmark=benchmark, yearList=yearList, date=date)
dfPyr = get_period_yearly_returns(df=df, fund=fund, benchmark=benchmark, yearList=yearList)
# Create graph
create_graph(df=df, fund=fund, benchmark=benchmark, yearList=yearList, date=date)
# perform multifactor regression regression over the longest year period in yearList, y = fund returns - rf
regOutput, r2 = df.regression(y, betas, max(yearList), date)
print()
print(rdf.head())
print()
# accessing a ReturnSeries
rsrsF = rdf.loc[:, fund]
print('rsrs data type: {}'.format(type(rsrsF)))
print()
print(rsrsF.head())
print()
rsrsBM = rdf.loc[:, benchmark]
rsrsRF = rdf.loc[:, rf_rate]
# functions that use years and monthEndDate use the 2 functions below from date_functions.py
endDate = dtf.get_month_end(rsrsF, date=date)
startDate = dtf.relative_start(endDate, years=3)
print('The sample for the 3 year statistics start at {} and ends on {} \n'.
format(startDate, endDate))
# calculate stats based on a ReturnSeries in the ReturnFrame
annualized_return_3Y, annualized_standard_dev_3Y = rsrsF.annualize(
years=3, monthEndDate=date)
print(
'The annualized return and standard deviation based on the last 3 years are:'
'\n Return: {:.2%} \n Standard Deviation: {:.2%}'.format(
annualized_return_3Y, annualized_standard_dev_3Y))
print('\n Without formatting numbers: \n {} , {}'.format(
annualized_return_3Y, annualized_standard_dev_3Y))
print()