def numerator(start_time, end_time):
# 营业利润=营业收入-营业成本-销售费用-管理费用-财务费用
opr_ttm = pitcache_getter('OPR_TTM', 10).get_tsdata(start_time, end_time)
opcost_ttm = pitcache_getter('OPCOST_TTM', 10).get_tsdata(start_time, end_time)
opexp_ttm = pitcache_getter('OPEXP_TTM', 10).get_tsdata(start_time, end_time)
adminexp_ttm = pitcache_getter('ADMINEXP_TTM', 10).get_tsdata(start_time, end_time)
fiexp_ttm = pitcache_getter('FIEXP_TTM', 10).get_tsdata(start_time, end_time)
return opr_ttm - opcost_ttm - opexp_ttm - adminexp_ttm - fiexp_ttm
def get_ev(start_time, end_time):
mkv_data = pitcache_getter('TOTAL_CMKV',
10).get_tsdata(start_time, end_time)
ncl_data = pitcache_getter('TNCL', 10).get_tsdata(start_time,
end_time).fillna(0)
cash_data = pitcache_getter('CASH', 10).get_tsdata(start_time,
end_time).fillna(0)
return mkv_data + ncl_data - cash_data
def inner(start_time, end_time):
cache_size = 10
share_data = pitcache_getter(share_name, cache_size).get_tsdata(
start_time, end_time)
price_data = pitcache_getter(price_name, cache_size).get_tsdata(
start_time, end_time)
data = share_data * price_data
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def inner(start_time, end_time):
start_time, end_time = trans_date(start_time, end_time)
shifted_start_time = get_calendar('stock.sse').shift_tradingdays(start_time, -period-1)
ret_data = pitcache_getter('CLOSE_DRET', 10).get_tsdata(shifted_start_time, end_time)
data = ret_data.rolling(period, min_periods=period).apply(func)
data = data.loc[(data.index >= start_time) & (data.index <= end_time)]
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def inner(start_time, end_time):
start_time, end_time = trans_date(start_time, end_time)
start_time_shifted = get_calendar('stock.sse').shift_tradingdays(start_time, -(lag+1))
ret_data = pitcache_getter(ret_source, lag+1).get_tsdata(start_time_shifted, end_time)
data = ret_data.rolling(lag, min_periods=lag).std()
latest_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(pitcache_getter('UNIVERSE', 10).get_csdata(latest_td).index)
data = data.reindex(columns=universe)
data = data.loc[(data.index>=start_time) & (data.index<=end_time)]
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def inner(start_time, end_time):
start_time, end_time = trans_date(start_time, end_time)
shifted_start_time = get_calendar('stock.sse').shift_tradingdays(start_time, -start_t-1)
period = start_t - end_t
price_data = pitcache_getter('CLOSE_ADJ', 10).get_tsdata(shifted_start_time, end_time)
data = price_data.shift(end_t).pct_change(period)
data = data.loc[(data.index >= start_time) & (data.index <= end_time)]
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def inner(start_time, end_time):
sql = '''
SELECT S.{level}, S.InfoPublDate, M.SecuCode
FROM LC_exgIndustry S, SecuMain M
WHERE S.CompanyCOde = M.CompanyCode AND
S.Standard = {standard} AND
M.SecuCategory = 1 AND
M.SecuMarket in (90, 83)
ORDER BY M.Secucode, S.InfoPublDate ASC
'''.format(level=INDUSTRY_LEVEL_MAP[class_level], standard=class_standard)
data = fetch_db_data(jydb, sql, ['data', 'time', 'symbol'])
data['data'] = data.data.map(translate_table)
data['symbol'] = data.symbol.apply(add_stock_suffix)
by_symbol = data.groupby('symbol')
tds = get_calendar('stock.sse').get_tradingdays(start_time, end_time)
data = by_symbol.apply(map2td, days=tds, timecol='time',
fillna={'data': lambda x: NaS, 'symbol': lambda x: x.symbol.iloc[0]})
data = data.pivot_table('data', index='time', columns='symbol',
aggfunc=lambda x: ';'.join(x))
check_agg_error(data)
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe).fillna(NaS).astype(np_unicode)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Error, data missed!')
return data
def get_liststatus(start_time, end_time):
'''
获取上市公司的上市状态，1表示正常上市，2表示暂停上市，3表示退市整理，4表示终止上市，NA表示非
正常上市状态
'''
sql = '''
SELECT M.Secucode, S.ChangeType, S.changeDate
FROM SECUMAIN M, LC_ListStatus S
WHERE M.INNERCODE = S.INNERCODE AND
M.SecuCategory = 1 AND
S.SecuMarket in (90, 83) AND
S.ChangeType != 9
ORDER BY M.SECUCODE ASC, S.changeDate ASC
'''
ls_data = fetch_db_data(jydb,
sql, ['symbol', 'data', 'time'],
dtypes={'data': 'int'})
# 原数据库中1表示上市，2表示暂停上市，3表示恢复上市，4表示退市,6表示退市整理
ls_map = {1: 1, 2: 2, 3: 1, 4: 4, 6: 3}
ls_data['data'] = ls_data.data.map(ls_map)
ls_data['symbol'] = ls_data.symbol.apply(add_stock_suffix)
by_symbol = ls_data.groupby('symbol')
tds = get_calendar('stock.sse').get_tradingdays(start_time, end_time)
ls_data = by_symbol.apply(map2td,
days=tds,
timecol='time',
fillna={'symbol': lambda x: x.symbol.iloc[0]})
ls_data = ls_data.pivot_table('data', index='time', columns='symbol')
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(
pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
ls_data = ls_data.reindex(columns=universe).sort_index(ascending=True)
if not check_completeness(ls_data.index, start_time, end_time):
raise ValueError('Error, data missed!')
return ls_data
def get_padjfactor(start_time, end_time):
'''
从数据库中获取复权因子
'''
sql = '''
SELECT A.ExDiviDate, A.RatioAdjustingFactor, M.SecuCode
FROM QT_AdjustingFactor A, SecuMain M
WHERE
A.InnerCode = M.InnerCode AND
M.secuMarket in (83, 90) AND
M.SECUCATEGORY = 1
ORDER BY M.SecuCode ASC, A.ExDiviDate ASC
'''
data = fetch_db_data(jydb,
sql, ['exdivdate', 'data', 'symbol'],
dtypes={'data': 'float64'})
data.symbol = data.symbol.apply(add_stock_suffix)
by_symbol = data.groupby('symbol')
tds = get_calendar('stock.sse').get_tradingdays(start_time, end_time)
data = by_symbol.apply(map2td,
days=tds,
timecol='exdivdate',
fillna={
'data': lambda x: 1,
'symbol': lambda x: x.symbol.iloc[0]
})
data = data.pivot_table('data', index='exdivdate', columns='symbol')
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(
pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe).sort_index(ascending=True).fillna(1)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def inner(start_time, end_time):
list_state = pitcache_getter('LIST_STATE',
10).get_tsdata(start_time, end_time)
valid_mask = logical_or(list_state == 1, list_state == 2)
data = func(start_time, end_time)
data[~valid_mask] = nan
return data
def equity_dropped_negetive(start_time, end_time):
'''
将归属母公司权益为负的公司权益设置为NA
'''
data = pitcache_getter('EQUITY5S', 10).get_tsdata(start_time, end_time)
data = data.where(data > 0, np.nan)
return data
def inner(start_time, end_time):
sql = '''
SELECT S.{share_type}, S.EndDate, M.SecuCode
FROM SecuMain M, LC_ShareStru S
WHERE M.CompanyCode = S.CompanyCode AND
M.SecuMarket in (83, 90) AND
M.SecuCategory = 1 AND
M.ListedState != 9
ORDER BY M.SecuCode ASC, S.EndDate ASC
'''.format(share_type=share_type)
data = fetch_db_data(jydb,
sql, ['data', 'time', 'symbol'],
dtypes={'data': 'float64'})
data.symbol = data.symbol.apply(add_stock_suffix)
data = data.drop_duplicates(['symbol', 'time'
]) # 若证券代码和时间相同则认为是相同数据，一般不会出现股本数据更正
by_symbol = data.groupby('symbol')
tds = get_calendar('stock.sse').get_tradingdays(start_time, end_time)
data = by_symbol.apply(map2td,
days=tds,
timecol='time',
fillna={'symbol': lambda x: x.symbol.iloc[0]})
data = data.pivot_table('data', index='time', columns='symbol')
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(
pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Error, data missed!')
return data
def ta_dropped_negetive(start_time, end_time):
'''
将总资产为负的总资产数据设置为NA
'''
data = pitcache_getter('TA5S', 10).get_tsdata(start_time, end_time)
data = data.where(data > 0, np.nan)
return data
def denominator(start_time, end_time):
"""
用于计算分母的数据，分母 = NI1S - NI5S
Parameter
---------
start_time: datetime like
开始时间
end_time: datetime like
结束时间
Return
------
out: pandas.DataFrame
"""
ni1s = pitcache_getter('NI1S', 10).get_tsdata(start_time, end_time)
ni5s = pitcache_getter('NI5S', 10).get_tsdata(start_time, end_time)
return ni1s - ni5s
def denominator(start_time, end_time):
"""
用于计算分母的数据，分母 = OPR1S - OPR5S
Parameter
---------
start_time: datetime like
开始时间
end_time: datetime like
结束时间
Return
------
out: pandas.DataFrame
"""
opr1s = pitcache_getter('OPR1S', 10).get_tsdata(start_time, end_time)
opr5s = pitcache_getter('OPR5S', 10).get_tsdata(start_time, end_time)
return opr1s - opr5s
def inner(start_time, end_time):
mkv_data = pitcache_getter(mkvdata_name,
50).get_tsdata(start_time, end_time)
mask = np_isclose(mkv_data, 0, 0.01)
data = mkv_data.where(~mask)
data = np_log(mkv_data)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def shift_processor(raw_data,
cols,
start_time,
end_time,
offset,
freq,
is_single_season=False):
"""
偏移数据处理函数，即计算每个日期内可以看到的给定偏移的数据，同时支持流量数据和存量
数据，支持的偏移频率包含[季度, 半年度, 年度]。对于存量数据，同时支持所有偏移频率；
对于季度数据，仅支持[季度, 年度]这两种偏移频率
Parameter
---------
raw_data: pandas.DataFrame
待处理的原始财报数据(即季报、半年报和年报)
cols: iterable
数据列名，元素依次为[证券代码, 数据, 更新时间, 报告期时间]
start_time: datetime like
计算结果开始时间
end_time: datetime like
计算结果结束时间
offset: int
往前推的期数，offset=1表示计算最近一期的数据，offset=2表示计算次近一期的数据，
以此类推
freq: int
数据推移的频率，仅支持[1, 2, 4]分别表示为季度、半年度和年度
is_single_season: boolean, default False
是否计算季度数据，仅当freq为1是才会启用，该选项适用于流量数据进行季度偏移的情况，
且目标是获取偏移后的单季度数据
Return
------
out: pandas.DataFrame
index为时间，columns为证券代码轴
"""
if freq == 1 and is_single_season: # 计算单季度数据
raw_data = calc_seasonly_data(raw_data, cols)
raw_data.symbol = raw_data.symbol.apply(add_stock_suffix)
data = process_fundamental_data(raw_data,
cols,
start_time,
end_time,
offset * freq + 3,
calc_offsetdata,
data_col=cols[1],
period_flag_col=cols[3],
offset=offset,
multiple=freq)
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(
pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def get_trade_status(start_time, end_time):
'''
获取股票的交易状态
Notes
-----
将成交量为0或者最高价等于最低价视为不能交易，返回值为1表示正常交易，0表示不能交易，NA表示未上市而不能交易
'''
sql = '''
SELECT S.TradingDay, S.TurnoverVolume, S.HighPrice, S.LowPrice, M.Secucode
FROM QT_DailyQuote S, SecuMain M
WHERE
S.InnerCode = M.InnerCode AND
M.SecuMarket in (83, 90) AND
S.TradingDay <= \'{end_time:%Y-%m-%d}\' AND
S.TradingDay >= \'{start_time:%Y-%m-%d}\' AND
M.SecuCategory = 1
ORDER BY M.Secucode ASC, S.TradingDay ASC
'''
start_time, end_time = trans_date(start_time, end_time)
offset = 30
start_time_shifted = get_calendar('stock.sse').shift_tradingdays(
start_time, -offset - 10)
sql = sql.format(start_time=start_time_shifted, end_time=end_time)
data = fetch_db_data(jydb,
sql, ['time', 'vol', 'high', 'low', 'symbol'],
dtypes={
'vol': 'float64',
'high': 'float64',
'low': 'float64'
})
data.symbol = data.symbol.apply(add_stock_suffix)
ma_vol = data.groupby('symbol', as_index=False).vol.rolling(
offset, min_periods=offset).mean()
ma_vol = ma_vol.reset_index(level=0, drop=True)
data = data.assign(ma_vol=ma_vol)
data = data.assign(flag=1)
# pdb.set_trace()
data.loc[np.isclose(data.ma_vol, 0, 0.1),
'ma_vol'] = np.nan # (上个交易日)移动平均成交量过低
data.ma_vol = data.vol / data.ma_vol
data.loc[np.isclose(data.vol, 0, 0.1), 'flag'] = 0 # 成家量过低，不可交易
data.loc[data.high == data.low, 'flag'] = 0 # 最高价等于最低价，不可交易
# 移动平均成交量过低，不可交易
data.loc[np.isclose(data.ma_vol, 0, 0.05) | (pd.isnull(data.ma_vol)),
'flag'] = 0
data = data.pivot_table('flag', index='time', columns='symbol')
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(
pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.loc[(data.index >= start_time)
& (data.index <= end_time)].reindex(columns=universe)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Error, data missed!')
return data
def inner(start_time, end_time):
data_group = [data_name_format.format(i=i) for i in range(1, period + 1)]
raw_data = query_group(data_group, start_time, end_time)
by_date = raw_data.groupby(level=0)
data = by_date.apply(calc_growth)
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def inner(start_time, end_time):
start_time_shifted = get_calendar('stock.sse').shift_tradingdays(
start_time, -freq - 10)
price_data = pitcache_getter(price_name,
50).get_tsdata(start_time_shifted,
end_time)
data = price_data.pct_change(freq)
data = data.loc[(data.index >= start_time) & (data.index <= end_time)]
# pdb.set_trace()
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def get_torate(start_time, end_time):
"""
计算换手率，计算公式为当日交易量/流通股数
Parameter
---------
start_time: datetime like
end_time: datetime like
Return
------
out: pandas.DataFrame
"""
volume_data = pitcache_getter('TO_VOLUME',
50).get_tsdata(start_time, end_time)
float_shares = pitcache_getter('FLOAT_SHARES',
50).get_tsdata(start_time, end_time)
data = volume_data / float_shares
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def get_suspend_state(start_time, end_time):
"""
获取股票停牌状态
Notes
-----
其中，0表示未停牌，1表示整日停牌，2表示日内部分交易时间停牌
"""
start_time, end_time = trans_date(start_time, end_time)
sql = '''
SELECT M.SecuCode, S.SuspendDate, S.ResumptionDate
FROM LC_SuspendResumption S, SecuMain M
WHERE
S.InnerCode = M.InnerCode AND
M.SecuMarket in (83, 90) AND
(S.ResumptionDate >= \'{start_time: %Y-%m-%d}\' OR
s.ResumptionDate = '1900-01-01') AND
S.suspenddate <= \'{end_time: %Y-%m-%d}\' AND
M.SecuCategory = 1
ORDER BY M.Secucode ASC, S.SuspendDate ASC
'''.format(start_time=start_time, end_time=end_time)
data = fetch_db_data(jydb, sql,
['symbol', 'suspend_date', 'resumption_date'])
data.symbol = data.symbol.apply(add_stock_suffix)
tds = get_calendar('stock.sse').get_tradingdays(start_time, end_time)
def process_per_symbol(df):
state = {}
for _, line in df.iterrows():
_, start, end = line
if start < end: # 正常超过一个交易日的停牌
state[start] = 1
state[end] = 0
elif end == pd.to_datetime('1900-01-01'): # 停牌至计算日还未复牌
state[start] = 1
else: # 日内停牌
state[start] = 2
state = pd.Series(state).sort_index()
return state
data = data.groupby('symbol').apply(process_per_symbol).unstack().T
data = data.fillna(method='ffill')
data = map2td(data, tds)
latest_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(
pitcache_getter('UNIVERSE', 10).get_csdata(latest_td).index)
data = data.reindex(columns=universe).fillna(0)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def numerator(start_time, end_time):
"""
用于计算分子数据，分子 = abs(NI5S)
Parameter
---------
start_time: datetime like
开始时间
end_time: datetime like
结束时间
Return
------
out: pandas.DataFrame
"""
ni5s = pitcache_getter('NI5S', 10).get_tsdata(start_time, end_time)
return np.abs(ni5s)
def inner(start_time, end_time):
start_time, end_time = trans_date(start_time, end_time)
start_time_shift = get_calendar('stock.sse').shift_tradingdays(start_time, -180)
nonlocal sql
sql = sql.format(code=index_symbol, start_time=start_time_shift, end_time=end_time)
data = fetch_db_data(jydb, sql, ['symbol', 'weight', 'time'], dtypes={'weight': 'float64'})
data.symbol = data.symbol.apply(add_stock_suffix)
tds = get_calendar('stock.sse').get_tradingdays(start_time, end_time)
data = data.pivot_table('weight', index='time', columns='symbol')
data = map2td(data, tds)
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Error, data missed!')
return data
def inner(start_time, end_time):
start_time, end_time = trans_date(start_time, end_time)
nonlocal sql
sql = sql.format(start_time=start_time, end_time=end_time)
data = fetch_db_data(jydb, sql, cols, dtypes=dtypes)
data.symbol = data.symbol.apply(add_stock_suffix)
if len(cols) == 4: # 当前数据为需要使用前收盘填充的数据
data.loc[data.data == 0, 'data'] = data['prevclose']
data = data.drop('prevclose', axis=1)
data = data.pivot_table('data', index='time', columns='symbol')
latest_td = get_calendar('stock.sse').latest_tradingday(
end_time, 'PAST')
universe = sorted(
pitcache_getter('UNIVERSE', 10).get_csdata(latest_td).index)
data = data.reindex(columns=universe).sort_index(ascending=True)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def inner(start_time, end_time):
start_time, end_time = trans_date(start_time, end_time)
cache_size = 100
offset = major_mul * minor_mul
threshold = 1e-5
start_time_shifted = get_calendar('stock.sse').\
shift_tradingdays(start_time, -offset - 20)
to_data = pitcache_getter('TO_RATE', cache_size).\
get_tsdata(start_time_shifted, end_time)
data = to_data.rolling(offset,
min_periods=offset).sum().dropna(how='all')
data[data <= threshold] = np_nan
data = data / major_mul
data = np_log(data)
data = data.loc[(data.index >= start_time) & (data.index <= end_time)]
if start_time > trans_date(DATA_START_DATE):
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
def ttm_processor(raw_data, cols, start_time, end_time):
"""
将原始的数据处理成TTM，仅支持流量类的数据，如利润表、现金流量表
Parameter
---------
raw_data: pandas.DataFrame
待处理的原始报表数据(即季报、半年报和年报)
cols: iterable
数据列名，元素依次为[证券代码, 数据, 更新时间, 报告期时间]
start_time: datetime like
计算结果的开始时间
end_time: datetime like
计算结果的结束时间
Return
------
out: pandas.DataFrame
index为时间，columns为证券代码轴
"""
raw_data = calc_seasonly_data(raw_data, cols)
# pdb.set_trace()
raw_data.symbol = raw_data.symbol.apply(add_stock_suffix)
data = process_fundamental_data(raw_data,
cols,
start_time,
end_time,
6,
calc_tnm,
data_col='data',
period_flag_col='rpt_date')
last_td = get_calendar('stock.sse').latest_tradingday(end_time, 'PAST')
universe = sorted(
pitcache_getter('UNIVERSE', 10).get_csdata(last_td).index)
data = data.reindex(columns=universe)
if not check_completeness(data.index, start_time, end_time):
raise ValueError('Data missed!')
return data
#!/usr/bin/env python # -*- coding:utf-8 """ Author: Hao Li Email: [email protected] Github: https://github.com/SAmmer0 Created: 2018-05-25 16:27 """ from sys import path as sys_path from os.path import dirname from tdtools import trans_date from pitdata.const import CALCULATION_FOLDER_PATH, DataType from pitdata import DataDescription, pitcache_getter if CALCULATION_FOLDER_PATH not in sys_path: sys_path.append(dirname(CALCULATION_FOLDER_PATH)) from derivate_template import divide_data_factory dd = DataDescription( divide_data_factory( pitcache_getter('EQUITY', 10).get_tsdata, pitcache_getter('TOTAL_CMKV', 10).get_tsdata), trans_date('2018-05-25'), DataType.PANEL_NUMERIC, ['EQUITY', 'TOTAL_CMKV', 'LIST_STATE'], '归属母公司的权益TTM/总市值')
#!/usr/bin/env python # -*- coding:utf-8 """ Author: Hao Li Email: [email protected] Github: https://github.com/SAmmer0 Created: 2018-06-07 15:06 """ from sys import path as sys_path from os.path import dirname import numpy as np from tdtools import trans_date from pitdata.const import CALCULATION_FOLDER_PATH, DataType from pitdata import DataDescription, pitcache_getter if CALCULATION_FOLDER_PATH not in sys_path: sys_path.append(dirname(CALCULATION_FOLDER_PATH)) from derivate_template import divide_data_factory dd = DataDescription( divide_data_factory( pitcache_getter('NI_TTM', 10).get_tsdata, pitcache_getter('TOTAL_SHARES', 10).get_tsdata), trans_date('2018-06-07'), DataType.PANEL_NUMERIC, ['NI_TTM', 'TOTAL_SHARES'], 'Earnings per share TTM')
Author: Hao Li Email: [email protected] Github: https://github.com/SAmmer0 Created: 2018-07-30 16:51 """ from sys import path as sys_path from os.path import dirname import numpy as np from tdtools import trans_date from pitdata.const import CALCULATION_FOLDER_PATH, DataType from pitdata import DataDescription, pitcache_getter if CALCULATION_FOLDER_PATH not in sys_path: sys_path.append(dirname(CALCULATION_FOLDER_PATH)) from derivate_template import divide_data_factory, abs_wrapper def numerator(start_time, end_time): # 营业利润=营业收入-营业成本-销售费用-管理费用-财务费用 opr_ttm = pitcache_getter('OPR_TTM', 10).get_tsdata(start_time, end_time) opcost_ttm = pitcache_getter('OPCOST_TTM', 10).get_tsdata(start_time, end_time) opexp_ttm = pitcache_getter('OPEXP_TTM', 10).get_tsdata(start_time, end_time) adminexp_ttm = pitcache_getter('ADMINEXP_TTM', 10).get_tsdata(start_time, end_time) fiexp_ttm = pitcache_getter('FIEXP_TTM', 10).get_tsdata(start_time, end_time) return opr_ttm - opcost_ttm - opexp_ttm - adminexp_ttm - fiexp_ttm dd = DataDescription(divide_data_factory(numerator, abs_wrapper(pitcache_getter('OPR_TTM', 10).get_tsdata)), trans_date('2018-07-30'), DataType.PANEL_NUMERIC, ['OPR_TTM', 'OPCOST_TTM', 'OPEXP_TTM', 'ADMINEXP_TTM', 'FIEXP_TTM'], '总营业利润率 = (营业收入-营业成本-销售费用-管理费用-财务费用) / abs(营业收入)')
