def debt( stock: str, date: datetime = datetime.now(), lookback_period: timedelta = timedelta(days=0), period: str = '', only_interest_expense=False, # any interest-bearing liability to qualify all_liabilities=False, # including accounts payable and deferred income long_term_debt=True, # and its associated currently due portion (measures capital structure) exclude_current_portion_long_term_debt=False # if true then also above should be true ): if long_term_debt: if not exclude_current_portion_long_term_debt: return fi.total_long_term_debt(stock=stock, date=date, lookback_period=lookback_period, period=period) else: return fi.long_term_debt_excluding_current_portion( stock=stock, date=date, lookback_period=lookback_period, period=period) if all_liabilities: return fi.total_liabilities(stock=stock, date=date, lookback_period=lookback_period, period=period) if only_interest_expense: return fi.interest_expense(stock=stock, date=date, lookback_period=lookback_period, period=period)
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 net_current_asset_value(stock: str, date: datetime = datetime.now(), lookback_period: timedelta = timedelta(days=0), period: str = ''): return fi.current_total_assets(stock=stock, date=date, lookback_period=lookback_period, period=period) \ - fi.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 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'