def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股DTOA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DTOA因子载荷
0. code
1. dtoa
如果计算失败, 返回None
"""
code = Utils.code_to_symbol(code)
report_date = Utils.get_fin_report_date(calc_date)
# 读取最新主要财务指标数据
fin_basic_data = Utils.get_fin_basic_data(code, report_date)
if fin_basic_data is None:
return None
# td为负债总额, ta为总资产
td = fin_basic_data['TotalLiability']
if np.isnan(td):
return None
ta = fin_basic_data['TotalAsset']
if np.isnan(ta):
return None
if abs(ta) < utils_con.TINY_ABS_VALUE:
return None
# dtoa = td / ta
dtoa = td / ta
return pd.Series([code, dtoa], index=['code', 'dtoa'])
def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股的BTOP因子载荷
Paramters:
--------
: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BTOP因子载荷
0. code
1. btop
如果计算失败, 返回None
"""
# 读取个股的财务数据
fin_report_date = Utils.get_fin_report_date(calc_date)
fin_basic_data = Utils.get_fin_basic_data(code, fin_report_date)
if fin_basic_data is None:
return None
# 读取个股的市值因子(LNCAP)
df_lncap = cls._LNCAP_Cache.get(Utils.datetimelike_to_str(calc_date, dash=False))
if df_lncap is None:
lncap_path = os.path.join(factor_ct.FACTOR_DB.db_path, risk_ct.LNCAP_CT.db_file)
df_lncap = Utils.read_factor_loading(lncap_path, Utils.datetimelike_to_str(calc_date, dash=False))
cls._LNCAP_Cache.set(Utils.datetimelike_to_str(calc_date, dash=False), df_lncap)
secu_lncap = df_lncap[df_lncap['id'] == Utils.code_to_symbol(code)]
if secu_lncap.empty:
return None
flncap = secu_lncap.iloc[0]['factorvalue']
# 账面市值比=净资产/市值
btop = (fin_basic_data['TotalAsset'] - fin_basic_data['TotalLiability']) * 10000 / np.exp(flncap)
return pd.Series([Utils.code_to_symbol(code), btop], index=['code', 'btop'])
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 _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
