def QA_data_fq_factor(code): bfq = QA_fetch_get_stock_day('ts', code, '1991-01-01', '', '00', 'pd') qfq = QA_fetch_get_stock_day('ts', code, '1991-01-01', '', '01', 'pd') hfq = QA_fetch_get_stock_day('ts', code, '1991-01-01', '', '02', 'pd') factor_frame = pd.DataFrame() factor_frame['qfqfactor'] = qfq['open'] / bfq['open'] factor_frame['hfqfactor'] = hfq['open'] / bfq['open'] factor_frame['bfqfactor'] = bfq['open'] / bfq['open'] return factor_frame
def QA_data_get_hfq(code, start, end): '使用网络数据进行复权/需要联网' xdxr_data = QA_fetch_get_stock_xdxr('tdx', code) bfq_data = QA_fetch_get_stock_day( 'tdx', code, '1990-01-01', str(datetime.date.today())).dropna(axis=0) return QA_data_make_hfq(bfq_data[start:end], xdxr_data)
def QA_data_fq_factor(code, start='1991-01-01', end=''): bfq = QA_fetch_get_stock_day('ts', code, start, end, '00', 'pd') qfq = QA_fetch_get_stock_day('ts', code, '1991-01-01', '', '01', 'pd') factor_frame = pd.DataFrame() factor_frame['qfqfactor'] = qfq['open'] / bfq['open'] return factor_frame