def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股ETOP因子载荷
Parameters:
--------
:param code: str
个股代码, 如SH600000, 600000
:param calc_date: datetime-like, str
计算日期, 格式: YYYY-MM-DD
:return: pd.Series
--------
个股的ETOP因子载荷
0. code
1. etop
如果计算失败, 返回None
"""
code = Utils.code_to_symbol(code)
# 读取个股的ttm净利润
ttm_fin_data = Utils.get_ttm_fin_basic_data(code, calc_date)
if ttm_fin_data is None:
return None
ttm_netprofit = ttm_fin_data['NetProfit']
if pd.isnull(ttm_netprofit):
return None
# 读取个股市值
lncap_path = os.path.join(factor_ct.FACTOR_DB.db_path, risk_ct.LNCAP_CT.db_file)
lncap_data = Utils.read_factor_loading(lncap_path, Utils.datetimelike_to_str(calc_date, dash=False), code)
if lncap_data.empty:
return None
secu_cap = np.exp(lncap_data['factorvalue'])
# etop = ttm净利润/市值
etop = ttm_netprofit * 10000 / secu_cap
return pd.Series([code, etop], index=['code', 'etop'])
def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股的成长因子，包含npg_ttm, opg_ttm
Parameters:
--------
:param code: str
个股代码，如600000或SH600000
:param calc_date: datetime-like or str
计算日期，格式YYYY-MM-DD, YYYYMMDD
:return: pd.Series
--------
成长类因子值
0. id: 证券代码
1. npg_ttm: 净利润增长率_TTM
2. opg_ttm: 营业收入增长率_TTM
若计算失败, 返回None
"""
code = Utils.code_to_symbol(code)
calc_date = Utils.to_date(calc_date)
# 读取最新的TTM财务数据
ttm_fin_data_latest = Utils.get_ttm_fin_basic_data(code, calc_date)
if ttm_fin_data_latest is None:
return None
# 读取去年同期TTM财务数据
try:
pre_date = datetime.datetime(calc_date.year-1, calc_date.month, calc_date.day)
except ValueError:
pre_date = calc_date - datetime.timedelta(days=366)
ttm_fin_data_pre = Utils.get_ttm_fin_basic_data(code, pre_date)
if ttm_fin_data_pre is None:
return None
# 计算成长类因子值
if abs(ttm_fin_data_pre['NetProfit']) < 0.1:
return None
npg_ttm = (ttm_fin_data_latest['NetProfit'] - ttm_fin_data_pre['NetProfit']) / abs(ttm_fin_data_pre['NetProfit'])
if abs(ttm_fin_data_pre['MainOperateRevenue']) < 0.1:
return None
opg_ttm = (ttm_fin_data_latest['MainOperateRevenue'] - ttm_fin_data_pre['MainOperateRevenue']) / abs(ttm_fin_data_pre['MainOperateRevenue'])
return Series([code, round(npg_ttm, 4), round(opg_ttm, 4)], index=['id', 'npg_ttm', 'opg_ttm'])
def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股的价值因子，包含ep_ttm, bp_lr, ocf_ttm
Parameters:
--------
:param code: str
个股代码：如600000或SH600000
:param calc_date: datetime-like or str
计算日期，格式YYYY-MM-DD, YYYYMMDD
:return: pd.Series
--------
价值类因子值
0. ep_ttm: TTM净利润/总市值
1. bp_lr: 净资产（最新财报）/总市值
2. ocf_ttm: TTM经营性现金流/总市值
若计算失败，返回None
"""
code = Utils.code_to_symbol(code)
calc_date = Utils.to_date(calc_date)
# 读取TTM财务数据
ttm_fin_data = Utils.get_ttm_fin_basic_data(code, calc_date)
if ttm_fin_data is None:
return None
# 读取最新财报数据
report_date = Utils.get_fin_report_date(calc_date)
fin_basic_data = Utils.get_fin_basic_data(code, report_date)
if fin_basic_data is None:
return None
# 计算总市值
mkt_daily = Utils.get_secu_daily_mkt(code,
calc_date,
fq=False,
range_lookup=True)
if mkt_daily.shape[0] == 0:
return None
cap_struct = Utils.get_cap_struct(code, calc_date)
if cap_struct is None:
return None
total_cap = cap_struct.total - cap_struct.liquid_b - cap_struct.liquid_h
total_mkt_cap = total_cap * mkt_daily.close
# 计算价值类因子
ep_ttm = ttm_fin_data[
'NetProfit'] * util_ct.FIN_DATA_AMOUNT_UNIT / total_mkt_cap
ocf_ttm = ttm_fin_data[
'NetOperateCashFlow'] * util_ct.FIN_DATA_AMOUNT_UNIT / total_mkt_cap
bp_lr = fin_basic_data[
'ShareHolderEquity'] * util_ct.FIN_DATA_AMOUNT_UNIT / total_mkt_cap
return Series([round(ep_ttm, 6),
round(bp_lr, 6),
round(ocf_ttm, 6)],
index=['ep_ttm', 'bp_lr', 'ocf_ttm'])
def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股EPFWD因子载荷
Parameters:
--------
:param code: str
个股代码, 如SH600000, 600000
:param calc_date: datetime-like, str
计算日期, 格式: YYYY-MM-DD
:return: pd.Series
--------
个股的EPFWD因子载荷
0. code
1. epfwd
如果计算失败, 返回None
"""
code = Utils.code_to_symbol(code)
# 读取个股的预期盈利数据
predictedearnings_data = Utils.get_consensus_data(
calc_date, code, ConsensusType.PredictedEarings)
if predictedearnings_data is None:
# 如果个股的预期盈利数据不存在, 那么代替ttm净利润
ttm_fin_data = Utils.get_ttm_fin_basic_data(code, calc_date)
if ttm_fin_data is None:
return None
predictedearnings_data = pd.Series(
[code, ttm_fin_data['NetProfit']],
index=['code', 'predicted_earnings'])
fpredictedearnings = predictedearnings_data['predicted_earnings']
if np.isnan(fpredictedearnings):
return None
# 读取个股市值
size_path = os.path.join(factor_ct.FACTOR_DB.db_path,
risk_ct.LNCAP_CT.db_file)
size_factor_loading = Utils.read_factor_loading(
size_path, Utils.datetimelike_to_str(calc_date, dash=False), code)
if size_factor_loading.empty:
return None
# epfwd = 盈利预期/市值
epfwd = fpredictedearnings * 10000.0 / np.exp(
size_factor_loading['factorvalue'])
return pd.Series([code, epfwd], index=['code', 'epfwd'])
def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股CETOP因子载荷
Parameters:
--------
:param code: str
个股代码, 如SH600000, 600000
:param calc_date: datetime-like, str
计算日期, 格式: YYYY-MM-DD
:return: pd.Series
--------
个股的CETOP因子载荷
0. code
1. cetop
如果计算失败, 返回None
"""
code = Utils.code_to_symbol(code)
# 读取个股的主要财务指标数据ttm值
ttm_fin_data = Utils.get_ttm_fin_basic_data(code, calc_date)
if ttm_fin_data is None:
return None
ttm_cash = ttm_fin_data['NetOperateCashFlow']
if np.isnan(ttm_cash):
return None
# 读取个股市值
lncap_path = os.path.join(factor_ct.FACTOR_DB.db_path,
risk_ct.LNCAP_CT.db_file)
lncap_data = Utils.read_factor_loading(
lncap_path, Utils.datetimelike_to_str(calc_date, dash=False), code)
if lncap_data.empty:
return None
secu_cap = np.exp(lncap_data['factorvalue'])
# cetop = 经营活动现金流ttm值/市值
cetop = ttm_cash * 10000 / secu_cap
return pd.Series([code, cetop], index=['code', 'cetop'])
def _get_prevN_years_finbasicdata(date, code, years):
"""
读取过去n年的主要财务指标数据, 其中每股数据会经过复权因子调整
:param date: datetime-like
日期
:param code: str
个股代码, 格式: SH600000
:param years: int
返回的报告期年数
:return: list of pd.Series
"""
year = date.year
month = date.month
if month in (1, 2, 3, 4):
# report_dates = [datetime.datetime(year-5, 12, 31),
# datetime.datetime(year-4, 12, 31),
# datetime.datetime(year-3, 12, 31),
# datetime.datetime(year-2, 12, 31)]
report_dates = [
datetime.datetime(year - n, 12, 31) for n in range(years, 1, -1)
]
is_ttm = True
elif month in (5, 6, 7, 8):
# report_dates = [datetime.datetime(year-5, 12, 31),
# datetime.datetime(year-4, 12, 31),
# datetime.datetime(year-3, 12, 31),
# datetime.datetime(year-2, 12, 31),
# datetime.datetime(year-1, 12, 31)]
report_dates = [
datetime.datetime(year - n, 12, 31) for n in range(years, 0, -1)
]
is_ttm = False
else:
# report_dates = [datetime.datetime(year-4, 12, 31),
# datetime.datetime(year-3, 12, 31),
# datetime.datetime(year-2, 12, 31),
# datetime.datetime(year-1, 12, 31)]
report_dates = [
datetime.datetime(year - n, 12, 31)
for n in range(years - 1, 0, -1)
]
is_ttm = True
df_mkt_data = Utils.get_secu_daily_mkt(code, end=date,
fq=True) # 个股复权行情, 用于调整每股数据
prevN_years_finbasicdata = []
for report_date in report_dates:
fin_basic_data = Utils.get_fin_basic_data(code,
report_date,
date_type='report_date')
if fin_basic_data is None:
return None
fin_basic_data = fin_basic_data.to_dict()
df_extract_mkt = df_mkt_data[
df_mkt_data.date <= report_date.strftime('%Y-%m-%d')]
if not df_extract_mkt.empty:
fq_factor = df_extract_mkt.iloc[-1]['factor']
# 调整每股数据
fin_basic_data[
'BasicEPS_adj'] = fin_basic_data['BasicEPS'] * fq_factor
fin_basic_data['UnitNetAsset_adj'] = fin_basic_data[
'UnitNetAsset'] * fq_factor
fin_basic_data['UnitNetOperateCashFlow_adj'] = fin_basic_data[
'UnitNetOperateCashFlow'] * fq_factor
# 计算调整后的主营业务收入
fin_basic_data['MainOperateRevenue_adj'] = fin_basic_data[
'MainOperateRevenue'] / fq_factor
else:
fin_basic_data['BasicEPS_adj'] = fin_basic_data['BasicEPS']
fin_basic_data['UnitNetAsset_adj'] = fin_basic_data['UnitNetAsset']
fin_basic_data['UnitNetOperateCashFlow_adj'] = fin_basic_data[
'UnitNetOperateCashFlow']
fin_basic_data['MainOperateRevenue_adj'] = fin_basic_data[
'MainOperateRevenue']
prevN_years_finbasicdata.append(fin_basic_data)
if is_ttm:
ttm_fin_basic_data = Utils.get_ttm_fin_basic_data(code, date)
if ttm_fin_basic_data is None:
return None
ttm_fin_basic_data = ttm_fin_basic_data.to_dict()
df_extract_mkt = df_mkt_data[
df_mkt_data.date <= ttm_fin_basic_data['ReportDate'].strftime(
'%Y-%m-%d')]
if not df_extract_mkt.empty:
fq_factor = df_extract_mkt.iloc[-1]['factor']
# 调整每股数据
ttm_fin_basic_data[
'BasicEPS_adj'] = ttm_fin_basic_data['BasicEPS'] * fq_factor
# 计算调整后的主营业务收入
ttm_fin_basic_data['MainOperateRevenue_adj'] = ttm_fin_basic_data[
'MainOperateRevenue'] / fq_factor
else:
ttm_fin_basic_data['BasicEPS_adj'] = ttm_fin_basic_data['BasicEPS']
ttm_fin_basic_data['MainOperateRevenue_adj'] = ttm_fin_basic_data[
'MainOperateRevenue']
prevN_years_finbasicdata.append(ttm_fin_basic_data)
return prevN_years_finbasicdata
