def val_signals(self,
variant='daily',
func=None,
shares_index=SHARES_DILUTED):
"""
Calculate valuation signals such as P/E and P/Sales ratios.
:param variant:
String with the frequency of the results. Valid options:
- 'daily': The result has daily data-points, same as share-prices.
- 'latest' The result is only for the latest share-price dates.
:param func:
Function to apply on a per-stock basis on the financial data,
before calculating the valuation signals. This is useful e.g. to
calculate multi-year averages of the Net Income and Revenue and
use those when calculating P/E and P/Sales ratios. This should
be a real function (not a lambda-function) because its name will
be used in the cache-filename. Example: `func=sf.avg_ttm_2y`
:param shares_index:
String with the column-name for the share-counts. SHARES_DILUTED
takes the potential diluting impact of stock-options into account,
so it results in more conservative valuation ratios than
SHARES_BASIC.
:return:
Pandas DataFrame
"""
# Load the required datasets.
# This is only really necessary if the cache-file needs refreshing,
# but it is easier to program like this and the overhead is small.
df_prices = self.load_shareprices(variant=variant)
df_income_ttm = self.load_income(variant='ttm')
df_balance_ttm = self.load_balance(variant='ttm')
df_cashflow_ttm = self.load_cashflow(variant='ttm')
# List of datasets used to determine if disk-cache must be refreshed.
datasets = [('shareprices', variant), ('income', 'ttm'),
('balance', 'ttm'), ('cashflow', 'ttm')]
# List of arguments used to uniquely identify the cache-file.
cache_ids = [variant, _func_name(func=func), shares_index]
# Create dict with disk-cache arguments.
cache_args = self._cache_args(datasets=datasets, cache_ids=cache_ids)
# Calculate the signals, or load the DataFrame from the disk-cache.
df_result = val_signals(df_prices=df_prices,
df_income_ttm=df_income_ttm,
df_balance_ttm=df_balance_ttm,
df_cashflow_ttm=df_cashflow_ttm,
shares_index=shares_index,
func=func,
**self._signal_args,
**cache_args)
return df_result
def fin_signals(self, variant='daily', func=None):
"""
Calculate financial signals such as Net Profit Margin, Debt Ratio, etc.
:param variant:
String with the frequency of the results. Valid options:
- 'quarterly': The result has 4 data-points per year.
- 'daily': The result has daily data-points, same as share-prices.
- 'latest' The result is only for the latest share-price dates.
:param func:
Function to apply on a per-stock basis after the signals have been
calculated, but before they have been reindexed to daily
data-points. This is useful e.g. to calculate multi-year averages.
This should be a real function (not a lambda-function) because its
name will be used in the cache-filename.
Example: `func=sf.avg_ttm_2y` to calculate 2-year averages.
Example: `func=sf.rel_change_ttm_1y` to calculate 1-year change.
:return:
Pandas DataFrame
"""
# Load the required datasets.
# This is only really necessary if the cache-file needs refreshing,
# but it is easier to program like this and the overhead is small.
df_income_ttm = self.load_income(variant='ttm')
df_balance_ttm = self.load_balance(variant='ttm')
df_cashflow_ttm = self.load_cashflow(variant='ttm')
# List of datasets used to determine if disk-cache must be refreshed.
datasets = [('income', 'ttm'), ('balance', 'ttm'), ('cashflow', 'ttm')]
# Load dataset with shareprices?
if variant in ['daily', 'latest']:
# Load share-prices for the given variant.
df_prices = self.load_shareprices(variant=variant)
# Append to the list of datasets we are using here.
datasets.append(('shareprices', variant))
elif variant == 'quarterly':
# Share-prices are not used to reindex the results.
df_prices = None
else:
# Raise exception.
msg = 'invalid arg variant={0}'.format(variant)
raise ValueError(msg)
# List of arguments used to uniquely identify the cache-file.
cache_ids = [variant, _func_name(func=func)]
# Create dict with disk-cache arguments.
cache_args = self._cache_args(datasets=datasets, cache_ids=cache_ids)
# Calculate the signals, or load the DataFrame from the disk-cache.
df_result = fin_signals(df_income_ttm=df_income_ttm,
df_balance_ttm=df_balance_ttm,
df_cashflow_ttm=df_cashflow_ttm,
df_prices=df_prices,
func=func,
**self._signal_args,
**cache_args)
return df_result
