def ohlson_o_score(stock: str, date: datetime = datetime.now(), lookback_period: timedelta = timedelta(days=0),
period: str = 'TTM'):
TA = financials.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
GNP = macroeconomic_analysis.gross_national_product_price_index(date)
TL = financials.total_liabilities(stock=stock, date=date, lookback_period=lookback_period, period=period)
WC = metrics.net_working_capital(stock=stock, date=date, lookback_period=lookback_period, period=period)
CL = financials.total_current_liabilities(stock=stock, date=date, lookback_period=lookback_period, period=period)
CA = financials.total_current_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
X = 1 if TL > TA else 0
NI = financials.net_income(stock=stock, date=date, lookback_period=lookback_period, period=period)
NI_prev = financials.net_income(stock=stock, date=date, lookback_period=lookback_period, period=period)
FFO = financials.cash_flow_operating_activities(stock=stock, date=date, lookback_period=lookback_period,
period=period)
Y = 1 if (NI < 0 and NI_prev < 0) else 0
return -1.32 - 0.407 * np.log(TA / GNP) + 6.03 * (TL / TA) - 1.43 * (WC / TA) + 0.0757 * (CL / CA) - 1.72 * X \
- 2.37 * (NI / TA) - 1.83 * (FFO / TL) + 0.285 * Y - 0.521 * ((NI - NI_prev) / (abs(NI) + abs(NI_prev)))
def capital_employed(stock: str, date: datetime = datetime.now(), lookback_period: timedelta = timedelta(days=0),
period: str = ''):
return fi.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period) \
- fi.current_total_liabilities(stock=stock, date=date, lookback_period=lookback_period, period=period)
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 piotroski_f_score(stock: str,
date: datetime = datetime.now(),
lookback_period: timedelta = timedelta(days=0),
period: str = 'TTM',
diluted_shares=True):
piotroski_dictio = {
'Profitability': {},
'Financial Leverage, Liquidity, and Source of Funds': {},
'Operating Efficiency': {},
'Piotroski F-Score': {
' ': {
' ': {0}
}
}
}
# Return on Assets (1 point if it is positive in the current year, 0 otherwise)
return_on_assets_current_year = ratios.return_on_assets(
stock=stock, date=date, lookback_period=lookback_period, period=period)
piotroski_dictio['Profitability']['Return on Assets'] = {
'Return on Assets Current Year':
'{:.4f}'.format(return_on_assets_current_year),
'ROA Positive in the Current Year ?':
return_on_assets_current_year > 0
}
# Operating Cash Flow (1 point if it is positive in the current year, 0 otherwise)
operating_cash_flow_current_year = financials.cash_flow_operating_activities(
stock=stock, date=date, lookback_period=lookback_period, period=period)
piotroski_dictio['Profitability']['Operating Cash Flow'] = {
'Operating Cash Flow Current Year':
'{:.2f}'.format(operating_cash_flow_current_year),
'OCF Positive in the Current Year ?':
operating_cash_flow_current_year > 0
}
# Change in Return of Assets (ROA) (1 point if ROA is higher in the current year compared to the previous one,
# 0 otherwise)
return_on_assets_previous_year = ratios.return_on_assets(
stock=stock,
date=date,
lookback_period=timedelta(days=365),
period=period)
piotroski_dictio['Profitability']['Change in Return of Assets'] = {
'Return on Assets Current Year':
'{:.4f}'.format(return_on_assets_current_year),
'Return on Assets Previous Year':
'{:.4f}'.format(return_on_assets_previous_year),
'ROA Current Year > ROA Previous Year ?':
return_on_assets_current_year > return_on_assets_previous_year
}
# Accruals (1 point if Operating Cash Flow/Total Assets is higher than ROA in the current year, 0 otherwise)
total_assets_current_year = financials.total_assets(
stock=stock, date=date, lookback_period=lookback_period, period=period)
accruals = operating_cash_flow_current_year / total_assets_current_year
piotroski_dictio['Profitability']['Accruals'] = {
'Operating Cash Flow Current Year':
'{}'.format(operating_cash_flow_current_year),
'Total Assets Current Year':
'{}'.format(total_assets_current_year),
'Accruals Current Year':
'{:.4f}'.format(accruals),
'ROA Current Year':
'{:.4f}'.format(return_on_assets_current_year),
'Accruals Current Year > ROA Current Year ?':
accruals > return_on_assets_current_year
}
# Change in Leverage (long-term) ratio (1 point if the ratio is lower this year compared to the previous one,
# 0 otherwise)
debt_to_assets_current_year = ratios.debt_ratio(
stock=stock, date=date, lookback_period=lookback_period, period=period)
debt_to_assets_previous_year = ratios.debt_ratio(
stock=stock,
date=date,
lookback_period=timedelta(days=365),
period=period)
piotroski_dictio['Financial Leverage, Liquidity, and Source of Funds'][
'Change in Leverage Ratio'] = {
'Debt to Assets Current Year':
'{:.4f}'.format(debt_to_assets_current_year),
'Debt to Assets Previous Year':
'{:.4f}'.format(debt_to_assets_current_year),
'D/A Current Year < D/A Previous Year ?':
debt_to_assets_current_year < debt_to_assets_previous_year
}
# Change in Current ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise)
current_ratio_current_year = ratios.current_ratio(
stock=stock, date=date, lookback_period=lookback_period, period=period)
current_ratio_previous_year = ratios.current_ratio(
stock=stock,
date=date,
lookback_period=timedelta(days=365),
period=period)
piotroski_dictio['Financial Leverage, Liquidity, and Source of Funds'][
'Change in Current Ratio'] = {
'Current Ratio Current Year':
'{:.4f}'.format(current_ratio_current_year),
'Current Ratio Previous Year':
'{:.4f}'.format(current_ratio_previous_year),
'CR Current Year > CR Previous Year ?':
current_ratio_current_year > current_ratio_previous_year
}
shares_current_year = financials.total_shares_outstanding(
stock=stock,
date=date,
lookback_period=lookback_period,
diluted_shares=diluted_shares,
period=period)
shares_previous_year = financials.total_shares_outstanding(
stock=stock,
date=date,
lookback_period=timedelta(days=365),
diluted_shares=diluted_shares,
period=period)
# Change in the number of shares (1 point if no new shares were issued during the last year)
piotroski_dictio['Financial Leverage, Liquidity, and Source of Funds'][
'Change in Number of Shares'] = {
'Shares Outstanding Current Year': shares_current_year,
'Shares Outstanding Previous Year': shares_previous_year,
'No New Shares Issued ?':
shares_current_year <= shares_previous_year
}
# Change in Gross Margin (1 point if it is higher in the current year compared to the previous one, 0 otherwise)
gross_margin_current_year = ratios.gross_profit_margin(
stock=stock, date=date, lookback_period=lookback_period, period=period)
gross_margin_previous_year = ratios.gross_profit_margin(
stock=stock,
date=date,
lookback_period=timedelta(days=365),
period=period)
piotroski_dictio['Operating Efficiency']['Gross Margin'] = {
'Gross Margin Current Year':
'{:.4f}'.format(gross_margin_current_year),
'Gross Margin Previous Year':
'{:.4f}'.format(gross_margin_previous_year),
'GM Current Year > GM Previous Year ?':
gross_margin_current_year > gross_margin_previous_year
}
# Change in Asset Turnover ratio (1 point if it is higher in the current year compared to the previous one,
# 0 otherwise)
asset_turnover_current_year = ratios.asset_turnover_ratio(
stock=stock, date=date, lookback_period=lookback_period, period=period)
asset_turnover_previous_year = ratios.asset_turnover_ratio(
stock=stock,
date=date,
lookback_period=timedelta(days=365),
period=period)
piotroski_dictio['Operating Efficiency']['Asset Turnover Ratio'] = {
'Asset Turnover Ratio Current Year':
'{:.4f}'.format(asset_turnover_current_year),
'Asset Turnover Ratio Previous Year':
'{:.4f}'.format(asset_turnover_previous_year),
'ATO Current Year > ATO Previous Year ?':
asset_turnover_current_year > asset_turnover_previous_year
}
number_of_trues = 0
for k, v in piotroski_dictio.items():
for kk, vv in v.items():
for kkk, vvv in vv.items():
if isinstance(vvv, np.bool_) and vvv:
number_of_trues = number_of_trues + 1
piotroski_dictio['Piotroski F-Score'][' '][' '] = number_of_trues
return piotroski_dictio
def beneish_m_score(stock: str,
date: datetime = datetime.now(),
lookback_period: timedelta = timedelta(days=0),
period: str = 'TTM',
describe=False):
current_net_accounts_receivable = fi.net_accounts_receivable(
stock=stock, date=date, lookback_period=lookback_period, period=period)
current_net_sales = fi.net_sales(stock=stock,
date=date,
lookback_period=lookback_period,
period=period)
current_receivables_to_sales = current_net_accounts_receivable / current_net_sales
previous_net_accounts_receivable = fi.net_accounts_receivable(
stock=stock,
date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period,
period=period)
previous_net_sales = fi.net_sales(
stock=stock,
date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period,
period=period)
previous_receivables_to_sales = previous_net_accounts_receivable / previous_net_sales
DSRI = current_receivables_to_sales / previous_receivables_to_sales
previous_gross_profit_margin = ratios.gross_profit_margin(
stock=stock,
date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period,
period=period)
current_gross_profit_margin = ratios.gross_profit_margin(
stock=stock, date=date, lookback_period=lookback_period, period=period)
GMI = previous_gross_profit_margin / current_gross_profit_margin
current_asset_quality = 1 - (fi.total_current_assets(
stock=stock, date=date, lookback_period=lookback_period, period=period
) + fi.net_property_plant_equipment(
stock=stock, date=date, lookback_period=lookback_period, period=period
) + fi.current_marketable_securities(
stock=stock, date=date, lookback_period=lookback_period,
period=period))
previous_asset_quality = 1 - (fi.total_current_assets(
stock=stock,
date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period,
period=period) + fi.net_property_plant_equipment(
stock=stock,
date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period,
period=period) + fi.current_marketable_securities(
stock=stock,
date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period,
period=period))
AQI = current_asset_quality / previous_asset_quality
SGI = fi.net_sales(stock=stock, date=date, lookback_period=lookback_period, period=period) \
/ fi.net_sales(stock=stock, date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period, period=period)
current_depreciation = fi.accumulated_depreciation_amortization(stock=stock, date=date,
lookback_period=lookback_period, period=period) \
/ (fi.net_property_plant_equipment(stock=stock, date=date, lookback_period=lookback_period,
period=period)
+ fi.accumulated_depreciation_amortization(stock=stock, date=date,
lookback_period=lookback_period,
period=period))
previous_depreciation = fi.accumulated_depreciation_amortization(stock=stock,
date=date - timedelta(
days=365 if period == 'FY' else 90),
lookback_period=lookback_period, period=period) \
/ (fi.net_property_plant_equipment(stock=stock,
date=date - timedelta(
days=365 if period == 'FY' else 90),
lookback_period=lookback_period, period=period)
+ fi.accumulated_depreciation_amortization(stock=stock, date=date - timedelta(
days=365 if period == 'FY' else 90), lookback_period=lookback_period, period=period))
DEPI = previous_depreciation / current_depreciation
SGAI = (fi.selling_general_administrative(stock=stock, date=date, lookback_period=lookback_period, period=period)
/ fi.net_sales(stock=stock, date=date, lookback_period=lookback_period, period=period)) \
/ (fi.selling_general_administrative(stock=stock, date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period, period=period)
/ fi.net_sales(stock=stock, date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period, period=period))
previous_leverage = (fi.total_current_liabilities(stock=stock,
date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period, period=period) +
fi.total_long_term_debt(stock=stock, date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period, period=period)) \
/ fi.total_assets(stock=stock, date=date - timedelta(days=365 if period == 'FY' else 90),
lookback_period=lookback_period, period=period)
current_leverage = (fi.total_current_liabilities(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)) \
/ fi.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
LVGI = current_leverage / previous_leverage
TATA = (fi.operating_income(stock=stock, date=date, lookback_period=lookback_period, period=period)
- fi.cash_flow_operating_activities(stock=stock, date=date, lookback_period=lookback_period, period=period)) \
/ fi.total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period)
if not describe:
return -4.84 + 0.92 * DSRI + 0.528 * GMI + 0.404 * AQI + 0.892 * SGI + 0.115 * DEPI - 0.172 * SGAI + 4.679 * TATA - 0.327 * LVGI
else:
return {'Inputs': {}}
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'
