def invested_capital(stock: str, date: datetime = datetime.now(), lookback_period: timedelta = timedelta(days=0),
period: str = '', operating_approach=True):
if operating_approach:
return net_working_capital(stock=stock, date=date, lookback_period=lookback_period, period=period,
exclude_current_portion_cash_and_debt=True) \
+ fi.net_property_plant_equipment(stock=stock, date=date, lookback_period=lookback_period, period=period) \
+ fi.total_intangible_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
else: # investing approach
return fi.current_total_liabilities(stock=stock, date=date, lookback_period=lookback_period, period=period) \
+ fi.long_term_debt_excluding_current_portion(stock, date, lookback_period=timedelta(days=0),
period=period) \
+ fi.total_shareholders_equity(stock=stock, date=date, lookback_period=lookback_period, period=period) \
+ fi.cash_flow_financing_activities(stock=stock, date=date, lookback_period=lookback_period,
period=period) \
+ fi.cash_flow_investing_activities(stock=stock, date=date, lookback_period=lookback_period,
period=period)
def weighted_average_cost_of_capital(
stock,
date=datetime.now(),
lookback_period: timedelta = timedelta(days=0),
lookback_lookback_period: timedelta = timedelta(days=365 * 5),
period: str = 'FY',
beta_period='Monthly',
benchmark: str = '^GSPC'):
from_date = date - lookback_period - lookback_lookback_period
to_date = date - lookback_period
dictio = {
'Common Equity':
(cost_of_equity_capm(stock=stock,
from_date=from_date,
to_date=to_date,
beta_period=beta_period,
benchmark=benchmark),
fi.total_shareholders_equity(stock=stock,
date=date,
lookback_period=lookback_period,
period=period)),
'Preferred Equity':
(cost_of_preferred_stock(stock=stock,
date=date,
lookback_period=lookback_period,
period=period),
fi.preferred_stock_value(stock=stock,
date=date,
lookback_period=lookback_period,
period=period)),
'Debt': (cost_of_debt(stock=stock,
date=date,
lookback_period=lookback_period,
period=period),
fi.total_long_term_debt(stock=stock,
date=date,
lookback_period=lookback_period,
period=period))
}
capitals = [np.nan_to_num(v[1]) for k, v in dictio.items()]
weights = [part / sum(capitals) for part in capitals]
costs = [np.nan_to_num(v[0]) for k, v in dictio.items()]
return np.sum([weight * cost for weight, cost in zip(weights, costs)])
def altman_z_score(stock: str,
date: datetime = datetime.now(),
lookback_period: timedelta = timedelta(days=0),
period: str = 'TTM'):
A = metrics.net_working_capital(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
B = financials.retained_earnings(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
C = metrics.earnings_before_interest_and_taxes(stock=stock, date=date, lookback_period=lookback_period,
period=period) \
/ financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
D = financials.total_shareholders_equity(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ financials.total_liabilities(stock=stock, date=date, lookback_period=lookback_period, period=period)
E = financials.net_sales(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
with open(
os.path.join(config.ROOT_DIR, config.DATA_DIR_NAME,
config.MARKET_TICKERS_DIR_NAME, "nasdaq_df.pickle"),
"rb") as f:
nasdaq_tickers = pickle.load(f).index
# for private manufacturing companies
if stock not in nasdaq_tickers and 'Manufacturing' not in metrics.get_stock_sector(
stock):
z_plus_score = 0.717 * A + 0.847 * B + 3.107 * C + 0.420 * D + 0.998 * E
if z_plus_score > 2.9:
return z_plus_score, 'safe zone'
elif z_plus_score < 1.23:
return z_plus_score, 'distress zone'
else:
return z_plus_score, 'grey zone'
# for foreign firms (i.e. all but US and Canada) and for non-manufacturing firms, both public and private
elif ('Other Countries' in metrics.get_stock_location(stock)) \
or ('Manufacturing' not in metrics.get_stock_sector(stock)):
if 'Other Countries' in metrics.get_stock_location(stock):
z_plus_plus_score = 3.25 + 6.56 * A + 3.26 * B + 6.72 * C + 1.05 * D
else:
z_plus_plus_score = 6.56 * A + 3.26 * B + 6.72 * C + 1.05 * D
if z_plus_plus_score > 2.6:
return z_plus_plus_score, 'safe zone'
elif z_plus_plus_score < 1.1:
return z_plus_plus_score, 'distress zone'
else:
return z_plus_plus_score, 'grey zone'
else: # for public manufacturing firms, original score
D = metrics.market_capitalization(
stock=stock,
date=date,
lookback_period=lookback_period,
period=period) / financials.total_liabilities(
stock=stock,
date=date,
lookback_period=lookback_period,
period=period)
z_score = 1.2 * A + 1.4 * B + 3.3 * C + 0.6 * D + 1.0 * E
if z_score > 2.99:
return z_score, 'safe zone'
elif z_score < 1.81:
return z_score, 'distress zone'
else:
return z_score, 'grey zone'
def altman_z_score(stock, date, lookback_period: timedelta = timedelta(days=0), period: str = 'TTM'):
"""
:param stock:
:param date:
:param lookback_period:
:param period:
:return:
* For **private manufacturing companies**, a Z-score > 2.9 indicates *safe zone*, while < 1.23 indicates *distress zone*,
and what's in between is the *grey zone*.
For foreign firms (i.e. all but US and Canada) and for non-manufacturing firms (both public and private),
a Z-score
+------------------------+------------+----------+----------+
| | Safe Zone | Grey Zone | Distress Zone|
+========================+============+==========+==========+
| Public, Manufacturing | > 2.99 | | < 1.81 |
+-------------------------+-------------+---------+----------|
| Private, Manufacturing | column 2 | column 3 | column 4 |
+------------------------+------------+----------+----------+
| Non-Manufacturing, Foreign | | | |
+------------------------+------------+----------+----------+
"""
A = metrics.net_working_capital(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
B = financials.retained_earnings(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
C = metrics.earnings_before_interest_and_taxes(stock=stock, date=date, lookback_period=lookback_period,
period=period) \
/ financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
D = financials.total_shareholders_equity(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ financials.total_liabilities(stock=stock, date=date, lookback_period=lookback_period, period=period)
E = financials.net_sales(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
public_tickers = object_model.Company.objects(exchange__in=['NASDAQ', 'NYSE', 'AMEX']).values_list('name')
manufacturing_tickers = object_model.Company.objects(sic_sector='MANUFACTURING').values_list('name')
# for private manufacturing companies
if stock not in public_tickers and 'Manufacturing' not in metrics.get_stock_sector(stock):
z_plus_score = 0.717 * A + 0.847 * B + 3.107 * C + 0.420 * D + 0.998 * E
if z_plus_score > 2.9:
return z_plus_score, 'safe zone'
elif z_plus_score < 1.23:
return z_plus_score, 'distress zone'
else:
return z_plus_score, 'grey zone'
# for foreign firms (i.e. all but US and Canada) and for non-manufacturing firms, both public and private
elif ('Other Countries' in metrics.get_stock_location(stock)) \
or ('Manufacturing' not in metrics.get_stock_sector(stock)):
if 'Other Countries' in metrics.get_stock_location(stock):
z_plus_plus_score = 3.25 + 6.56 * A + 3.26 * B + 6.72 * C + 1.05 * D
else:
z_plus_plus_score = 6.56 * A + 3.26 * B + 6.72 * C + 1.05 * D
if z_plus_plus_score > 2.6:
return z_plus_plus_score, 'safe zone'
elif z_plus_plus_score < 1.1:
return z_plus_plus_score, 'distress zone'
else:
return z_plus_plus_score, 'grey zone'
else: # for public manufacturing firms, original score
D = metrics.market_capitalization(stock=stock, date=date, lookback_period=lookback_period,
period=period) / financials.total_liabilities(stock=stock, date=date,
lookback_period=lookback_period,
period=period)
z_score = 1.2 * A + 1.4 * B + 3.3 * C + 0.6 * D + 1.0 * E
if z_score > 2.99:
return z_score, 'safe zone'
elif z_score < 1.81:
return z_score, 'distress zone'
else:
return z_score, 'grey zone'