def net_credit_sales(stock: str, date: datetime = datetime.now(), lookback_period: timedelta = timedelta(days=0), period: str = ''): return fi.net_sales(stock=stock, date=date, lookback_period=lookback_period, period=period) \ - fi.net_accounts_receivable(stock=stock, date=date, lookback_period=lookback_period, period=period)
def gross_profit(stock: str, date: datetime = datetime.now(), lookback_period: timedelta = timedelta(days=0), period: str = ''): return fi.net_sales(stock=stock, date=date, lookback_period=lookback_period, period=period) \ - fi.cost_of_goods_services(stock=stock, date=date, lookback_period=lookback_period, period=period)
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: 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'