def market_capitalization(stock: str, diluted_shares: bool = False, date: datetime = datetime.now(),
lookback_period: timedelta = timedelta(days=0), period: str = 'Q'):
shares_outstanding = fi.total_shares_outstanding(stock=stock, date=date, lookback_period=lookback_period,
period=period, diluted_shares=diluted_shares)
output = market_price(stock, date, lookback_period) * shares_outstanding * 1000000 # TODO hotfix!
print('Market Capitalization for {} on the {} is: {}'.format(stock, date, output))
return output
def dividend_per_share(stock: str, date: datetime = datetime.now(), lookback_period: timedelta = timedelta(days=0),
period: str = '', diluted_shares: bool = True, deduct_preferred_dividends: bool = False):
dividends_paid = fi.payments_for_dividends(stock=stock, date=date, lookback_period=lookback_period, period=period)
dividends_paid -= abs(fi.preferred_dividends(stock=stock, date=date, lookback_period=lookback_period,
period=period)) if deduct_preferred_dividends else 0
shares_outstanding = fi.total_shares_outstanding(stock=stock, diluted_shares=diluted_shares, date=date,
lookback_period=lookback_period, period=period)
return abs(dividends_paid) / shares_outstanding
def cash_flow_per_share(cash_flow_metric,
stock,
date=datetime.today(),
lookback_period=timedelta(days=0),
period: str = '',
diluted_shares=True):
cash_flow = cash_flow_metric(stock=stock,
date=date,
lookback_period=lookback_period,
period=period)
shares_outstanding = fi.total_shares_outstanding(
stock=stock,
date=date,
lookback_period=lookback_period,
period=period,
diluted_shares=diluted_shares)
return cash_flow / shares_outstanding
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 valuation_wrapper(
model_type: partial, # Gordon Growth, Two Stage, H Model, Three Stage
# For Three-Stage Model, specify transitionary growth periods (5 by default)
# For all models except GGM, specify initial growth periods (5 by default)
model_metric: Callable, # Dividend, EBITDA, OCF...
stock: str,
date: datetime = datetime.now(),
lookback_period: timedelta = timedelta(days=0),
period: str = 'FY',
diluted_shares: bool = False):
shares_outstanding = fi.total_shares_outstanding(
stock=stock,
date=date,
lookback_period=lookback_period,
period=period,
diluted_shares=diluted_shares)
discount_rate = weighted_average_cost_of_capital(
stock=stock, date=date, lookback_period=lookback_period, period=period)
print('WACC is {}'.format(discount_rate))
if model_metric == me.dividend_per_share:
current_cash_flow = me.dividend_per_share(
stock=stock,
date=date,
lookback_period=lookback_period,
period=period,
diluted_shares=diluted_shares)
else:
current_cash_flow = me.cash_flow_per_share(
cash_flow_metric=model_metric,
stock=stock,
date=date,
lookback_period=lookback_period,
period=period,
diluted_shares=diluted_shares)
initial_growth_rate = cash_flow_growth_rate(
cash_flow_type=partial(me.cash_flow_per_share, model_metric),
stock=stock,
date=date,
lookback_period=lookback_period,
period=period)
print('Initial Growth Rate is {}'.format(initial_growth_rate))
terminal_growth_rate = growth_rate_PRAT_model(
stock,
date=datetime.now(),
lookback_period=timedelta(days=0),
period=period)
print('PRAT Terminal Growth Rate is {}'.format(terminal_growth_rate))
# The perpetuity growth rate is typically between the historical inflation rate of 2-3% and the historical GDP growth rate of 4-5%.
# If you assume a perpetuity growth rate in excess of 5%, you are basically saying that you expect the company's growth to outpace the economy's growth forever.
terminal_growth_rate = min(terminal_growth_rate,
HISTORICAL_GDP_GROWTH_RATE)
terminal_growth_rate = max(terminal_growth_rate, HISTORICAL_INFLATION_RATE)
print('Adujsted Terminal Growth Rate is {}'.format(terminal_growth_rate))
partial_function = partial(model_type,
current_cash_flow=current_cash_flow,
discount_rate=discount_rate,
terminal_growth_rate=terminal_growth_rate)
if model_type.func == gordon_growth_model:
if model_metric == me.dividend_per_share:
return partial_function()
else:
return partial_function() / shares_outstanding
else:
if model_metric == me.dividend_per_share:
return partial_function(initial_growth_rate=initial_growth_rate)
else:
return partial_function(initial_growth_rate=initial_growth_rate) \
/ shares_outstanding