def testWeeksDaysAlgebra(self):
twoWeeks = Period(length=2, units=TimeUnits.Weeks)
oneWeek = Period(length=1, units=TimeUnits.Weeks)
threeDays = Period(length=3, units=TimeUnits.Days)
oneDay = Period(length=1, units=TimeUnits.Days)
n = 2
flag = twoWeeks / n == oneWeek
self.assertTrue(
flag, "division error: {0} / {1:d}"
" not equal to {2}".format(twoWeeks, n, oneWeek))
n = 7
flag = oneWeek / 7 == oneDay
self.assertTrue(
flag, "division error: {0} / {1:d}"
" not equal to {2}".format(oneWeek, n, oneDay))
sum = threeDays
sum += oneDay
flag = sum == Period(length=4, units=TimeUnits.Days)
self.assertTrue(
flag, "sum error: {0}"
" + {1}"
" != {2}".format(threeDays, oneDay,
Period(length=4, units=TimeUnits.Days)))
sum += oneWeek
flag = sum == Period(length=11, units=TimeUnits.Days)
self.assertTrue(
flag, "sum error: {0}"
" + {1}"
" + {2}"
" != {3}".format(threeDays, oneDay, oneWeek,
Period(length=11, units=TimeUnits.Days)))
sevenDays = Period(length=7, units=TimeUnits.Days)
flag = sevenDays.length() == 7
self.assertTrue(
flag, "normalization error: sevenDays.length"
" is {0:d}"
" instead of 7".format(sevenDays.length()))
flag = sevenDays.units() == TimeUnits.Days
self.assertTrue(
flag, "normalization error: sevenDays.units"
" is {0:d}"
" instead of {1:d}".format(sevenDays.units(), TimeUnits.Days))
normalizedSevenDays = sevenDays.normalize()
flag = normalizedSevenDays.length() == 1
self.assertTrue(
flag, "normalization error: normalizedSevenDays.length"
" is {0:d}"
" instead of 1".format(normalizedSevenDays.length()))
flag = normalizedSevenDays.units() == TimeUnits.Weeks
self.assertTrue(
flag, "normalization error: TwelveMonths.units"
" is {0:d}"
" instead of {1:d}".format(normalizedSevenDays.units(),
TimeUnits.Weeks))
def prepare_data(engine: SqlEngine,
factors: Union[Transformer, Iterable[object]],
start_date: str,
end_date: str,
frequency: str,
universe: Universe,
benchmark: int,
warm_start: int = 0):
if warm_start > 0:
p = Period(frequency)
p = Period(length=-warm_start * p.length(), units=p.units())
start_date = advanceDateByCalendar('china.sse', start_date,
p).strftime('%Y-%m-%d')
dates = makeSchedule(start_date,
end_date,
frequency,
calendar='china.sse',
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Forward)
dates = [d.strftime('%Y-%m-%d') for d in dates]
horizon = map_freq(frequency)
if isinstance(factors, Transformer):
transformer = factors
else:
transformer = Transformer(factors)
factor_df = engine.fetch_factor_range(universe,
factors=transformer,
dates=dates).sort_values(
['trade_date', 'code'])
alpha_logger.info("factor data loading finished")
return_df = engine.fetch_dx_return_range(universe,
dates=dates,
horizon=horizon)
alpha_logger.info("return data loading finished")
industry_df = engine.fetch_industry_range(universe, dates=dates)
alpha_logger.info("industry data loading finished")
benchmark_df = engine.fetch_benchmark_range(benchmark, dates=dates)
alpha_logger.info("benchmark data loading finished")
df = pd.merge(factor_df, return_df, on=['trade_date', 'code']).dropna()
df = pd.merge(df, benchmark_df, on=['trade_date', 'code'], how='left')
df = pd.merge(df, industry_df, on=['trade_date', 'code'])
df['weight'] = df['weight'].fillna(0.)
return dates, df[['trade_date', 'code', 'dx']], df[[
'trade_date', 'code', 'weight', 'isOpen', 'industry_code', 'industry'
] + transformer.names]
def _map_horizon(frequency: str) -> int:
parsed_period = Period(frequency)
unit = parsed_period.units()
length = parsed_period.length()
if unit == TimeUnits.BDays or unit == TimeUnits.Days:
return length - 1
elif unit == TimeUnits.Weeks:
return 5 * length - 1
elif unit == TimeUnits.Months:
return 22 * length - 1
else:
raise ValueError(
'{0} is an unrecognized frequency rule'.format(frequency))
def prepare_data(engine: SqlEngine,
factors: Union[Transformer, Iterable[object]],
start_date: str,
end_date: str,
frequency: str,
universe: Universe,
benchmark: int,
warm_start: int = 0,
fit_target: Union[Transformer, object] = None):
if warm_start > 0:
p = Period(frequency)
p = Period(length=-warm_start * p.length(), units=p.units())
start_date = advanceDateByCalendar('china.sse', start_date,
p).strftime('%Y-%m-%d')
dates = makeSchedule(start_date,
end_date,
frequency,
calendar='china.sse',
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Forward)
dates = [d.strftime('%Y-%m-%d') for d in dates]
horizon = map_freq(frequency)
if isinstance(factors, Transformer):
transformer = factors
else:
transformer = Transformer(factors)
factor_df = engine.fetch_factor_range(universe,
factors=transformer,
dates=dates).sort_values(
['trade_date', 'code'])
alpha_logger.info("factor data loading finished")
if fit_target is None:
target_df = engine.fetch_dx_return_range(universe,
dates=dates,
horizon=horizon)
else:
one_more_date = advanceDateByCalendar('china.sse', dates[-1],
frequency)
target_df = engine.fetch_factor_range_forward(universe,
factors=fit_target,
dates=dates +
[one_more_date])
target_df = target_df[target_df.trade_date.isin(dates)]
target_df = target_df.groupby('code').apply(
lambda x: x.fillna(method='pad'))
alpha_logger.info("fit target data loading finished")
industry_df = engine.fetch_industry_range(universe, dates=dates)
alpha_logger.info("industry data loading finished")
benchmark_df = engine.fetch_benchmark_range(benchmark, dates=dates)
alpha_logger.info("benchmark data loading finished")
df = pd.merge(factor_df, target_df, on=['trade_date', 'code']).dropna()
df = pd.merge(df, benchmark_df, on=['trade_date', 'code'], how='left')
df = pd.merge(df, industry_df, on=['trade_date', 'code'])
df['weight'] = df['weight'].fillna(0.)
df.dropna(inplace=True)
return dates, df[[
'trade_date', 'code', 'dx'
]], df[['trade_date', 'code', 'weight', 'industry_code', 'industry'] +
transformer.names]
def fetch_predict_phase(engine,
alpha_factors: Union[Transformer, Iterable[object]],
ref_date,
frequency,
universe,
batch=1,
neutralized_risk: Iterable[str] = None,
risk_model: str = 'short',
pre_process: Iterable[object] = None,
post_process: Iterable[object] = None,
warm_start: int = 0,
fillna: str = None,
fit_target: Union[Transformer, object] = None):
if isinstance(alpha_factors, Transformer):
transformer = alpha_factors
else:
transformer = Transformer(alpha_factors)
p = Period(frequency)
p = Period(length=-(warm_start + batch - 1) * p.length(), units=p.units())
start_date = advanceDateByCalendar('china.sse', ref_date, p,
BizDayConventions.Following)
dates = makeSchedule(start_date,
ref_date,
frequency,
calendar='china.sse',
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Backward)
horizon = map_freq(frequency)
factor_df = engine.fetch_factor_range(universe,
factors=transformer,
dates=dates)
if fillna:
factor_df = factor_df.groupby('trade_date').apply(
lambda x: x.fillna(x.median())).reset_index(drop=True).dropna()
else:
factor_df = factor_df.dropna()
if fit_target is None:
target_df = engine.fetch_dx_return_range(universe,
dates=dates,
horizon=horizon)
else:
one_more_date = advanceDateByCalendar('china.sse', dates[-1],
frequency)
target_df = engine.fetch_factor_range_forward(universe,
factors=fit_target,
dates=dates +
[one_more_date])
target_df = target_df[target_df.trade_date.isin(dates)]
target_df = target_df.groupby('code').apply(
lambda x: x.fillna(method='pad'))
names = transformer.names
if neutralized_risk:
risk_df = engine.fetch_risk_model_range(universe,
dates=dates,
risk_model=risk_model)[1]
used_neutralized_risk = list(set(neutralized_risk).difference(names))
risk_df = risk_df[['trade_date', 'code'] +
used_neutralized_risk].dropna()
train_x = pd.merge(factor_df, risk_df, on=['trade_date', 'code'])
train_x = pd.merge(train_x,
target_df,
on=['trade_date', 'code'],
how='left')
risk_exp = train_x[neutralized_risk].values.astype(float)
else:
train_x = pd.merge(factor_df,
target_df,
on=['trade_date', 'code'],
how='left')
risk_exp = None
train_x.dropna(inplace=True, subset=train_x.columns[:-1])
x_values = train_x[names].values.astype(float)
y_values = train_x[['dx']].values.astype(float)
date_label = pd.DatetimeIndex(train_x.trade_date).to_pydatetime()
dates = np.unique(date_label)
if dates[-1] == dt.datetime.strptime(ref_date, '%Y-%m-%d'):
end = dates[-1]
start = dates[-batch] if batch <= len(dates) else dates[0]
left_index = bisect.bisect_left(date_label, start)
right_index = bisect.bisect_right(date_label, end)
this_raw_x = x_values[left_index:right_index]
this_raw_y = y_values[left_index:right_index]
sub_dates = date_label[left_index:right_index]
if risk_exp is not None:
this_risk_exp = risk_exp[left_index:right_index]
else:
this_risk_exp = None
ne_x = factor_processing(this_raw_x,
pre_process=pre_process,
risk_factors=this_risk_exp,
post_process=post_process)
ne_y = factor_processing(this_raw_y,
pre_process=pre_process,
risk_factors=this_risk_exp,
post_process=post_process)
inner_left_index = bisect.bisect_left(sub_dates, end)
inner_right_index = bisect.bisect_right(sub_dates, end)
ne_x = ne_x[inner_left_index:inner_right_index]
ne_y = ne_y[inner_left_index:inner_right_index]
left_index = bisect.bisect_left(date_label, end)
right_index = bisect.bisect_right(date_label, end)
codes = train_x.code.values[left_index:right_index]
else:
ne_x = None
ne_y = None
codes = None
ret = dict()
ret['x_names'] = transformer.names
ret['predict'] = {
'x': pd.DataFrame(ne_x, columns=transformer.names),
'code': codes,
'y': ne_y.flatten()
}
return ret
def fetch_train_phase(engine,
alpha_factors: Union[Transformer, Iterable[object]],
ref_date,
frequency,
universe,
batch=1,
neutralized_risk: Iterable[str] = None,
risk_model: str = 'short',
pre_process: Iterable[object] = None,
post_process: Iterable[object] = None,
warm_start: int = 0,
fit_target: Union[Transformer, object] = None) -> dict:
if isinstance(alpha_factors, Transformer):
transformer = alpha_factors
else:
transformer = Transformer(alpha_factors)
p = Period(frequency)
p = Period(length=-(warm_start + batch) * p.length(), units=p.units())
start_date = advanceDateByCalendar('china.sse', ref_date, p,
BizDayConventions.Following)
dates = makeSchedule(start_date,
ref_date,
frequency,
calendar='china.sse',
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Backward)
horizon = map_freq(frequency)
factor_df = engine.fetch_factor_range(universe,
factors=transformer,
dates=dates)
if fit_target is None:
target_df = engine.fetch_dx_return_range(universe,
dates=dates,
horizon=horizon)
else:
one_more_date = advanceDateByCalendar('china.sse', dates[-1],
frequency)
target_df = engine.fetch_factor_range_forward(universe,
factors=fit_target,
dates=dates +
[one_more_date])
target_df = target_df[target_df.trade_date.isin(dates)]
target_df = target_df.groupby('code').apply(
lambda x: x.fillna(method='pad'))
df = pd.merge(factor_df, target_df, on=['trade_date', 'code']).dropna()
target_df, factor_df = df[['trade_date', 'code',
'dx']], df[['trade_date', 'code'] +
transformer.names]
target_df, dates, date_label, risk_exp, x_values, y_values, _, _, codes = \
_merge_df(engine, transformer.names, factor_df, target_df, universe, dates, risk_model,
neutralized_risk)
if dates[-1] == dt.datetime.strptime(ref_date, '%Y-%m-%d'):
pyFinAssert(
len(dates) >= 2, ValueError,
"No previous data for training for the date {0}".format(ref_date))
end = dates[-2]
start = dates[-batch - 1] if batch <= len(dates) - 1 else dates[0]
else:
end = dates[-1]
start = dates[-batch] if batch <= len(dates) else dates[0]
index = (date_label >= start) & (date_label <= end)
this_raw_x = x_values[index]
this_raw_y = y_values[index]
this_code = codes[index]
if risk_exp is not None:
this_risk_exp = risk_exp[index]
else:
this_risk_exp = None
ne_x = factor_processing(this_raw_x,
pre_process=pre_process,
risk_factors=this_risk_exp,
post_process=post_process)
ne_y = factor_processing(this_raw_y,
pre_process=pre_process,
risk_factors=this_risk_exp,
post_process=post_process)
ret = dict()
ret['x_names'] = transformer.names
ret['train'] = {
'x': pd.DataFrame(ne_x, columns=transformer.names),
'y': ne_y,
'code': this_code
}
return ret
def fetch_predict_phase(engine,
alpha_factors: Iterable[object],
ref_date,
frequency,
universe,
batch,
neutralized_risk: Iterable[str] = None,
risk_model: str = 'short',
pre_process: Iterable[object] = None,
post_process: Iterable[object] = None,
warm_start: int = 0):
transformer = Transformer(alpha_factors)
p = Period(frequency)
p = Period(length=-(warm_start + batch) * p.length(), units=p.units())
start_date = advanceDateByCalendar('china.sse', ref_date, p,
BizDayConventions.Following)
dates = makeSchedule(start_date,
ref_date,
frequency,
calendar='china.sse',
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Backward)
factor_df = engine.fetch_factor_range(universe,
factors=transformer,
dates=dates).dropna()
names = transformer.names
if neutralized_risk:
risk_df = engine.fetch_risk_model_range(universe,
dates=dates,
risk_model=risk_model)[1]
used_neutralized_risk = list(set(neutralized_risk).difference(names))
risk_df = risk_df[['trade_date', 'code'] +
used_neutralized_risk].dropna()
train_x = pd.merge(factor_df, risk_df, on=['trade_date', 'code'])
risk_exp = train_x[neutralized_risk].values.astype(float)
x_values = train_x[names].values.astype(float)
else:
train_x = factor_df.copy()
risk_exp = None
date_label = pd.DatetimeIndex(factor_df.trade_date).to_pydatetime()
dates = np.unique(date_label)
if dates[-1] == dt.datetime.strptime(ref_date, '%Y-%m-%d'):
end = dates[-1]
start = dates[-batch]
# index = (date_label >= start) & (date_label <= end)
left_index = bisect.bisect_left(date_label, start)
right_index = bisect.bisect_right(date_label, end)
this_raw_x = x_values[left_index:right_index]
sub_dates = date_label[left_index:right_index]
if risk_exp is not None:
this_risk_exp = risk_exp[left_index:right_index]
else:
this_risk_exp = None
ne_x = factor_processing(this_raw_x,
pre_process=pre_process,
risk_factors=this_risk_exp,
post_process=post_process)
inner_left_index = bisect.bisect_left(sub_dates, end)
inner_right_index = bisect.bisect_right(sub_dates, end)
ne_x = ne_x[inner_left_index:inner_right_index]
left_index = bisect.bisect_left(date_label, end)
right_index = bisect.bisect_right(date_label, end)
codes = train_x.code.values[left_index:right_index]
else:
ne_x = None
codes = None
ret = dict()
ret['x_names'] = transformer.names
ret['predict'] = {'x': ne_x, 'code': codes}
return ret
def fetch_train_phase(engine,
alpha_factors: Iterable[object],
ref_date,
frequency,
universe,
batch,
neutralized_risk: Iterable[str] = None,
risk_model: str = 'short',
pre_process: Iterable[object] = None,
post_process: Iterable[object] = None,
warm_start: int = 0) -> dict:
transformer = Transformer(alpha_factors)
p = Period(frequency)
p = Period(length=-(warm_start + batch + 1) * p.length(), units=p.units())
start_date = advanceDateByCalendar('china.sse', ref_date, p,
BizDayConventions.Following)
dates = makeSchedule(start_date,
ref_date,
frequency,
calendar='china.sse',
dateRule=BizDayConventions.Following,
dateGenerationRule=DateGeneration.Backward)
horizon = _map_horizon(frequency)
factor_df = engine.fetch_factor_range(universe,
factors=transformer,
dates=dates)
return_df = engine.fetch_dx_return_range(universe,
dates=dates,
horizon=horizon)
df = pd.merge(factor_df, return_df, on=['trade_date', 'code']).dropna()
return_df, factor_df = df[['trade_date', 'code',
'dx']], df[['trade_date', 'code', 'isOpen'] +
transformer.names]
return_df, dates, date_label, risk_exp, x_values, y_values, _, _ = \
_merge_df(engine, transformer.names, factor_df, return_df, universe, dates, risk_model, neutralized_risk)
if dates[-1] == dt.datetime.strptime(ref_date, '%Y-%m-%d'):
end = dates[-2]
start = dates[-batch - 1]
else:
end = dates[-1]
start = dates[-batch]
index = (date_label >= start) & (date_label <= end)
this_raw_x = x_values[index]
this_raw_y = y_values[index]
if risk_exp is not None:
this_risk_exp = risk_exp[index]
else:
this_risk_exp = None
ne_x = factor_processing(this_raw_x,
pre_process=pre_process,
risk_factors=this_risk_exp,
post_process=post_process)
ne_y = factor_processing(this_raw_y,
pre_process=pre_process,
risk_factors=this_risk_exp,
post_process=post_process)
ret = dict()
ret['x_names'] = transformer.names
ret['train'] = {'x': ne_x, 'y': ne_y}
return ret
def testYearsMonthsAlgebra(self):
oneYear = Period(length=1, units=TimeUnits.Years)
sixMonths = Period(length=6, units=TimeUnits.Months)
threeMonths = Period(length=3, units=TimeUnits.Months)
n = 4
flag = oneYear / n == threeMonths
self.assertTrue(
flag, "division error: {0} / {1:d}"
" not equal to {2}".format(oneYear, n, threeMonths))
n = 2
flag = oneYear / n == sixMonths
self.assertTrue(
flag, "division error: {0} / {1:d}"
" not equal to {2}".format(oneYear, n, sixMonths))
sum = threeMonths
sum += sixMonths
flag = sum == Period(length=9, units=TimeUnits.Months)
self.assertTrue(
flag, "sum error: {0}"
" + {1}"
" != {2}".format(threeMonths, sixMonths,
Period(length=9, units=TimeUnits.Months)))
sum += oneYear
flag = sum == Period(length=21, units=TimeUnits.Months)
self.assertTrue(
flag, "sum error: {0}"
" + {1}"
" + {2}"
" != {3}".format(threeMonths, sixMonths, oneYear,
Period(length=21, units=TimeUnits.Months)))
twelveMonths = Period(length=12, units=TimeUnits.Months)
flag = twelveMonths.length() == 12
self.assertTrue(
flag, "normalization error: TwelveMonths.length"
" is {0:d}"
" instead of 12".format(twelveMonths.length()))
flag = twelveMonths.units() == TimeUnits.Months
self.assertTrue(
flag, "normalization error: TwelveMonths.units"
" is {0:d}"
" instead of {1:d}".format(twelveMonths.units(), TimeUnits.Months))
normalizedTwelveMonths = Period(length=12, units=TimeUnits.Months)
normalizedTwelveMonths = normalizedTwelveMonths.normalize()
flag = normalizedTwelveMonths.length() == 1
self.assertTrue(
flag, "normalization error: TwelveMonths.length"
" is {0:d}"
" instead of 1".format(twelveMonths.length()))
flag = normalizedTwelveMonths.units() == TimeUnits.Years
self.assertTrue(
flag, "normalization error: TwelveMonths.units"
" is {0:d}"
" instead of {1:d}".format(twelveMonths.units(), TimeUnits.Years))
thirtyDays = Period(length=30, units=TimeUnits.Days)
normalizedThirtyDays = thirtyDays.normalize()
flag = normalizedThirtyDays.units() == TimeUnits.Days
self.assertTrue(
flag, "normalization error: ThirtyDays.units"
" is {0:d}"
" instead of {1:d}".format(normalizedThirtyDays.units(),
TimeUnits.Days))
thirtyBDays = Period(length=30, units=TimeUnits.BDays)
normalizedThirtyBDays = thirtyBDays.normalize()
flag = normalizedThirtyBDays.units() == TimeUnits.BDays
self.assertTrue(
flag, "normalization error: ThirtyBDays.units"
" is {0:d}"
" instead of {1:d}".format(normalizedThirtyBDays.units(),
TimeUnits.BDays))
