def calc_rotc(end_date):
#end_date = context.now
quarter = dao_u.get_report_quarter(end_date)
q = query(
financials.financial_indicator.ebit,
financials.financial_indicator.net_working_capital,
# financials.financial_indicator.working_capital,
financials.balance_sheet.accts_receivable,
financials.balance_sheet.other_accts_receivable,
financials.balance_sheet.prepayment,
financials.balance_sheet.inventory,
financials.financial_indicator.non_interest_bearing_current_debt,
financials.balance_sheet.long_term_equity_investment,
financials.balance_sheet.net_fixed_assets
)
rotc_df = get_financials(q, quarter = quarter, interval = '1q')
# rotc_df.fillna({'accts_receivable':0,'other_accts_receivable':0,'prepayment':0,'inventory':0,'non_interest_bearing_current_debt':0,'long_term_equity_investment':0}, inplace=True)
# shengqigongshi 版
# 净营运资本 = 应收账款 + 其他应收款 + 预付账款 + 存货 - 无息流动负债 + 长期股权投资
rotc_df['tangible_capital_2'] = rotc_df['accts_receivable'] + rotc_df['other_accts_receivable'] + rotc_df['prepayment'] + rotc_df['inventory'] - rotc_df['non_interest_bearing_current_debt'] + rotc_df['long_term_equity_investment'] + rotc_df['net_fixed_assets']
# 海外版
rotc_df['tangible_capital'] = rotc_df['net_working_capital'] + rotc_df['net_fixed_assets']
# rotc_df['rotc'] = rotc_df['ebit']/rotc_df['tc'] * 100
rotc_df['rotc'] = list(map(_calc_rotc_1, rotc_df['ebit'], rotc_df['tangible_capital']))
# 参考
rotc_df['rotc_2'] = rotc_df['ebit']/rotc_df['tangible_capital_2'] * 100
rotc_df.sort_values(by='rotc', ascending=False, inplace=True)
rotc_df['score_1'] = range(1, len(rotc_df.index) + 1)
rotc_df['rotc'] = list(map(_calc_rotc_2, rotc_df['ebit'], rotc_df['tangible_capital']))
return rotc_df
def _get_roa_roe_quarter_report(stocks, today, n_report_quarters, is_roa):
quarter = dao_u.get_report_quarter(today)
fin_df = get_financials(
(query(financials.financial_indicator.annual_return_on_asset_net_profit) if is_roa else query(financials.financial_indicator.annual_return_on_equity))
.filter(
financials.stockcode.in_(stocks)
), quarter = quarter, interval = str(n_report_quarters) + 'q'
)
roe_list = []
if len(stocks) == 1:
roe_list.append(fin_df.mean())
else:
for s in stocks:
roe_list.append(fin_df[s].mean())
return pd.Series(roe_list, index = stocks)
def get_market_pb(today=None, ignore_bank=False):
if today == None:
today = dao_u.get_last_date()
quarter = dao_u.get_report_quarter(today)
df_equity = get_financials(query(
fundamentals.balance_sheet.equity_parent_company).filter(
fundamentals.balance_sheet.equity_parent_company > 0),
quarter=quarter,
interval='1q')
q = query(
fundamentals.eod_derivative_indicator.market_cap,
#fundamentals.eod_derivative_indicator.a_share_market_val,
fundamentals.balance_sheet.equity_parent_company).filter(
fundamentals.balance_sheet.equity_parent_company > 0)
if ignore_bank:
q = q.filter(
sql.not_(
fundamentals.eod_derivative_indicator.stockcode.in_(
BANK_STOCKS)))
df_cap = get_fundamentals(q, entry_date=today, interval='1d')
df_x = df_cap.iloc[:, 0]
# fillna: how about df_x.combine_first(df_equity)? performance issue
# df_x.combine_first(df_equity)
null_equities = list(df_x[df_x['equity_parent_company'].isnull()].index)
for s in null_equities:
if hasattr(df_equity, s):
df_x.ix[s, 'equity_parent_company'] = df_equity.ix[
0, s] #df_equity.ix[s,'equity_parent_company']
df_x2 = df_x[df_x['market_cap'].notnull()]
df_x2 = df_x2[df_x2['equity_parent_company'].notnull()]
total_equity = df_x2['equity_parent_company'].sum()
market_cap = df_x2['market_cap'].sum()
return market_cap / total_equity
def calc_ebit_to_ev(end_date):
#end_date = context.now
quarter = dao_u.get_report_quarter(end_date)
q = query(
financials.financial_indicator.ebit,
financials.balance_sheet.minority_interest,
financials.financial_indicator.interest_bearing_debt,
financials.balance_sheet.total_liabilities,
financials.financial_indicator.non_interest_bearing_current_debt,
financials.financial_indicator.non_interest_bearing_non_current_debt,
financials.balance_sheet.cash
)
yield_df = get_financials(q, quarter = quarter, interval = '1q')
market_cap_df = get_fundamentals(
query(
fundamentals.eod_derivative_indicator.market_cap,
fundamentals.eod_derivative_indicator.ev_2
), entry_date = end_date + datetime.timedelta(days=-1), interval = '1d')
market_cap_df = market_cap_df.iloc[:,0]
yield_df = pd.concat([yield_df, market_cap_df], axis=1)
# shengqigongshi 版
# interest_bearing_debt = total_liabilities - non_interest_bearing_current_debt - non_interest_bearing_non_current_debt
yield_df['ev_my'] = yield_df['market_cap'] + yield_df['minority_interest'] + (yield_df['total_liabilities'] - yield_df['non_interest_bearing_current_debt'] - yield_df['non_interest_bearing_non_current_debt'])
# # 长投版
# yield_df['ev_my'] = yield_df['market_cap'] + yield_df['interest_bearing_debt'] - yield_df['cash']
# # 海外版
# yield_df['ev_my'] = yield_df['market_cap'] + yield_df['interest_bearing_debt']
yield_df['yield'] = yield_df['ebit']/yield_df['ev_my'] * 100
# RQ 的参考
yield_df['yield_2'] = yield_df['ebit']/yield_df['ev_2']
yield_df.sort_values(by='yield', ascending=False, inplace=True)
yield_df['score_2'] = range(1, len(yield_df.index) + 1)
return yield_df