def get_benchmark(self, benchmark_ticker='^GSPC'):
if benchmark_ticker == 'sp500' or benchmark_ticker == '^GSPC':
_ticker = '^GSPC'
else:
_ticker = '^GSPC' # S&P 500 as default
# create security class object for benchmark ticker
_benchmark = Security(_ticker, start=self.min_date, end=self.max_date)
# calculate returns for the benchmark
_benchmark.data = returns_column(df=_benchmark.data, column='Close')
self.benchmark = _benchmark
def get_returns(self, column='Total'):
try:
self.data
except AttributeError:
self.get_timeseries()
else:
pass
self.data_growth = returns_column(df=self.data_growth, column=column)
# get date range from the transaction list
self.min_date = min(self.transactions.Date.min(),
self.payments.Date.min())
self.max_date = datetime.datetime.now()
# make a list of days between min and max date as index for timeseries df
date_index = pd.date_range(self.min_date, self.max_date, freq='D')
_ts = pd.Series(range(len(date_index)), index=date_index)
# create timeseries df from date index and wallet entries
_df_ts = _ts.to_frame('Day')
#
# join in daily price change data for each security
cols = []
for security in self.tickers:
_series = self.securities[security].data['Close']
_col_name = str(_series.name) + "_" + security
_series = _series.rename(_col_name)
_df_ts = _df_ts.join(_series, how='left',
rsuffix='').fillna(method='ffill')
_df_ts = returns_column(df=_df_ts,
column=_col_name,
outname=security)
cols.append(_col_name)
self.returns = _df_ts.drop(cols, axis=1)
def get_benchmark(self, benchmark_ticker='^GSPC'):
self.min_date = self.data.index.min()
self.max_date = min(self.data.index.max(), datetime.datetime.now())
if benchmark_ticker == 'sp500' or benchmark_ticker == '^GSPC':
_ticker = '^GSPC'
else:
_ticker = '^GSPC' # S&P 500 as default
# create security class object for benchmark ticker
_benchmark = Security(_ticker, start=self.min_date, end=self.max_date)
# calculate returns for the benchmark
_benchmark.data = returns_column(df=_benchmark.data,
column='Close',
uselogs=True)
self.benchmark = _benchmark
def get_returns(self, column='Close'):
self.data = returns_column(df=self.data, column=column)
def get_returns(self, column='Total'):
self.data = returns_column(df=self.data, column=column)