def _cal_pe_ratio_1_by_manual(order_book_id):
ret = []
raw_day_reports = _day_metrics(order_book_id)
raw_quarter_reports = _quarter_metrics(order_book_id)
enddate_report_map = _enddate_reports_map(raw_quarter_reports)
day_df = DataFrame(raw_day_reports).fillna(numpy.nan)
market_caps = day_df.market_cap.values
tradedates = day_df.tradedate.values
for i in range(len(tradedates)):
t = int(to_datetime(tradedates[i]).strftime('%Y%m%d'))
ret_value = {'tradedate': t}
latest_enddates = _latest_enddates(t)
report = None
for latest_enddate in latest_enddates:
latest_report = enddate_report_map.get(latest_enddate)
if latest_report is None:
continue
ann_date = latest_report.get('announce_date')
if t >= ann_date:
report = latest_report
ret_value['announce_date'] = ann_date
ret_value['end_date'] = latest_enddate
break
if report is not None:
nppc_value = report.get('net_profit_parent_company')
if nppc_value is not None and nppc_value != 0:
market_cap = market_caps[i]
ret_value['pe_ratio_1'] = round(market_cap / nppc_value, 4)
ret_value['net_profit_parent_company'] = nppc_value
ret.append(ret_value)
return ret
def _cal_ev_by_manual(order_book_id):
ret = []
raw_day_reports = _day_metrics(order_book_id)
raw_quarter_reports = _quarter_metrics(order_book_id)
enddate_report_map = _enddate_reports_map(raw_quarter_reports)
day_df = DataFrame(raw_day_reports).fillna(numpy.nan)
tradedates = day_df.tradedate.values
val_stk_rights = day_df.val_of_stk_right.values
for i in range(len(tradedates)):
t = int(to_datetime(tradedates[i]).strftime('%Y%m%d'))
ret_value = {'tradedate': t}
latest_enddates = _latest_enddates(t)
report = None
for latest_enddate in latest_enddates:
latest_report = enddate_report_map.get(latest_enddate)
if latest_report is None:
continue
ann_date = latest_report.get('announce_date')
if t >= ann_date:
report = latest_report
ret_value['announce_date'] = ann_date
ret_value['end_date'] = latest_enddate
break
ret_value['ev'] = 0
val_stk_value = val_stk_rights[i]
if val_stk_value is not None:
ret_value['ev'] += val_stk_value
if report is not None:
debt = report.get('interest_bearing_debt')
if debt is not None:
ret_value['ev'] += debt
ret_value['interest_bearing_debt'] = debt
ret.append(ret_value)
return ret
def test_four_straight_quarter(self):
quarter_metrics = _quarter_metrics('000001.XSHE')
calc_four_straight_quarter = _four_straight_quarter(quarter_metrics,
'net_profit')
calc_df = DataFrame(calc_four_straight_quarter)
calc_df = calc_df[calc_df['announce_date'] <= 20160812]
calc_df['four_straight_net_profit'] = calc_df.apply(
lambda row: float(row['net_profit']), axis=1)
manual_df = read_csv('recal_by_manual/four_straight_net_profit.csv')
manual_df = manual_df[manual_df['announce_date'] <= 20160812]
self.assertEqual(manual_df['four_straight_net_profit'].equals(
calc_df['four_straight_net_profit']), True)
def _cal_peg_ratio_by_manual(order_book_id):
ret = []
raw_quarter_reports = _quarter_metrics(order_book_id)
enddate_report_map = _enddate_reports_map(raw_quarter_reports)
pe_ratio_2_by_manual = _cal_four_latest_by_manual(order_book_id, 'net_profit_parent_company', 'pe_ratio_2')
for i in range(len(pe_ratio_2_by_manual)):
tradedate = pe_ratio_2_by_manual[i]['tradedate']
ret_value = {'tradedate': tradedate}
pe_ratio_2_report = pe_ratio_2_by_manual[i]
last_year = int(tradedate / 10000) - 1
latest_annual_report = enddate_report_map.get(int(str(last_year) + QUARTER_ENDDATE_MAP[4]))
four_latest_report = pe_ratio_2_by_manual[i]
pe_ratio_2_value = pe_ratio_2_report.get('pe_ratio_2')
latest_annual_value = latest_annual_report.get('net_profit_parent_company') if latest_annual_report is not None else 0
four_latest_value = four_latest_report.get('net_profit_parent_company')
if None not in (pe_ratio_2_value, latest_annual_value, four_latest_value) and latest_annual_value != 0:
return_inc_g = (four_latest_value - latest_annual_value) / latest_annual_value * 100
if return_inc_g != 0:
ret_value['peg_ratio'] = round(pe_ratio_2_value / return_inc_g, 4)
ret.append(ret_value)
return ret
def _cal_four_latest_by_manual(order_book_id, metric_name, recal_metric_name):
ret = []
raw_day_reports = _day_metrics(order_book_id)
raw_quarter_reports = _quarter_metrics(order_book_id)
four_latest_reports = _four_latest_quarter(raw_quarter_reports, metric_name)
enddate_four_latest_map = _enddate_reports_map(four_latest_reports)
day_df = DataFrame(raw_day_reports).fillna(numpy.nan)
market_caps = day_df.market_cap.values
tradedates = day_df.tradedate.values
for i in range(len(tradedates)):
t = int(to_datetime(tradedates[i]).strftime('%Y%m%d'))
ret_value = {'tradedate': t}
latest_enddates = _latest_enddates(t)
report = None
for latest_enddate in latest_enddates:
latest_report = enddate_four_latest_map.get(latest_enddate)
if latest_report is None:
continue
ann_date = latest_report.get('announce_date')
if t >= ann_date:
report = latest_report
ret_value['announce_date'] = ann_date
ret_value['end_date'] = latest_enddate
break
if report is None:
ret.append(ret_value)
continue
metric_value = report.get(metric_name)
if metric_value is None or metric_value == 0:
ret.append(ret_value)
continue
ret_value[metric_name] = metric_value
ret_value[recal_metric_name] = round(market_caps[i] / metric_value, 4)
ret.append(ret_value)
return ret
def _get_quarter_metrics(order_book_id):
return _quarter_metrics(order_book_id)