class IEXEarnings(DataSet):
'''
"actualEPS": 2.46,
"consensusEPS": 2.36,
"announceTime": "AMC",
"numberOfEstimates": 34,
"EPSSurpriseDollar": 0.1,
"EPSReportDate": "2019-04-30",
"fiscalPeriod": "Q1 2019",
"fiscalEndDate": "2019-03-31",
"yearAgo": 2.73,
"yearAgoChangePercent": -0.0989
'''
announceTime = Column(object_dtype, missing_value='')
fiscalPeriod = Column(object_dtype, missing_value='')
EPSReportDate = Column(datetime64ns_dtype,
missing_value=np.datetime64('1970-01-01'))
fiscalEndDate = Column(datetime64ns_dtype,
missing_value=np.datetime64('1970-01-01'))
actualEPS = Column(float64_dtype, missing_value=np.nan)
consensusEPS = Column(float64_dtype, missing_value=np.nan)
numberOfEstimates = Column(float64_dtype, missing_value=np.nan)
EPSSurpriseDollar = Column(float64_dtype, missing_value=np.nan)
yearAgo = Column(float64_dtype, missing_value=np.nan)
yearAgoChangePercent = Column(float64_dtype, missing_value=np.nan)
_loader = IEXEarningsLoader()
@classmethod
def get_loader(cls):
return cls._loader
def infer_dataset(data_path, fields=None):
columns = {}
with pd.HDFStore(data_path) as store:
dtypes = store['/dtypes']
for k, v in dtypes.iteritems():
if fields is None or k in fields:
if v == 'O':
columns[k] = Column(v, missing_value='NA')
else:
columns[k] = Column(v)
return type("Dataset", (DataSet, ), columns)
class USEquityPricing(DataSet):
"""
Dataset representing daily trading prices and volumes.
"""
open = Column(float64_dtype)
high = Column(float64_dtype)
low = Column(float64_dtype)
close = Column(float64_dtype)
volume = Column(float64_dtype)
@staticmethod
def get_loader():
return _loader
def new_dataset(expr, missing_values, domain):
"""
Creates or returns a dataset from a blaze expression.
Parameters
----------
expr : Expr
The blaze expression representing the values.
missing_values : frozenset((name, value) pairs
Association pairs column name and missing_value for that column.
This needs to be a frozenset rather than a dict or tuple of tuples
because we want a collection that's unordered but still hashable.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
Returns
-------
ds : type
A new dataset type.
Notes
-----
This function is memoized. repeated calls with the same inputs will return
the same type.
"""
missing_values = dict(missing_values)
class_dict = {'ndim': 2 if SID_FIELD_NAME in expr.fields else 1}
for name, type_ in expr.dshape.measure.fields:
# Don't generate a column for sid or timestamp, since they're
# implicitly the labels if the arrays that will be passed to pipeline
# Terms.
if name in (SID_FIELD_NAME, TS_FIELD_NAME):
continue
type_ = datashape_type_to_numpy(type_)
if can_represent_dtype(type_):
col = Column(
type_,
missing_values.get(name, NotSpecified),
)
else:
col = NonPipelineField(name, type_)
class_dict[name] = col
if 'domain' in class_dict:
raise ValueError("Got a column named 'domain' in new_dataset(). "
"'domain' is reserved.")
class_dict['domain'] = domain
name = expr._name
if name is None:
name = next(_new_names)
# unicode is a name error in py3 but the branch is only hit
# when we are in python 2.
if PY2 and isinstance(name, unicode): # pragma: no cover # noqa
name = name.encode('utf-8')
return type(name, (DataSet, ), class_dict)
def test_construction_error_message(self):
with self.assertRaises(ValueError) as exc:
Column(dtype=datetime64ns_dtype, currency_aware=True)
self.assertEqual(
str(exc.exception),
'Columns cannot be constructed with currency_aware=True, '
'dtype=datetime64[ns]. Currency aware columns must have a float64 '
'dtype.',
)
def new_dataset(expr, deltas, missing_values):
"""
Creates or returns a dataset from a pair of blaze expressions.
Parameters
----------
expr : Expr
The blaze expression representing the first known values.
deltas : Expr
The blaze expression representing the deltas to the data.
missing_values : frozenset((name, value) pairs
Association pairs column name and missing_value for that column.
This needs to be a frozenset rather than a dict or tuple of tuples
because we want a collection that's unordered but still hashable.
Returns
-------
ds : type
A new dataset type.
Notes
-----
This function is memoized. repeated calls with the same inputs will return
the same type.
"""
missing_values = dict(missing_values)
columns = {}
for name, type_ in expr.dshape.measure.fields:
# Don't generate a column for sid or timestamp, since they're
# implicitly the labels if the arrays that will be passed to pipeline
# Terms.
if name in (SID_FIELD_NAME, TS_FIELD_NAME):
continue
type_ = datashape_type_to_numpy(type_)
if can_represent_dtype(type_):
col = Column(
type_,
missing_values.get(name, NotSpecified),
)
else:
col = NonPipelineField(name, type_)
columns[name] = col
name = expr._name
if name is None:
name = next(_new_names)
# unicode is a name error in py3 but the branch is only hit
# when we are in python 2.
if PY2 and isinstance(name, unicode): # noqa
name = name.encode('utf-8')
return type(name, (DataSet, ), columns)
class IEXCompany(DataSet):
symbol = Column(object_dtype, missing_value='')
companyName = Column(object_dtype, missing_value='')
exchange = Column(object_dtype, missing_value='')
industry = Column(object_dtype, missing_value='')
website = Column(object_dtype, missing_value='')
description = Column(object_dtype, missing_value='')
CEO = Column(object_dtype, missing_value='')
issueType = Column(object_dtype, missing_value='')
sector = Column(object_dtype, missing_value='')
# tags = Column(object_dtype, missing_value='')
_loader = IEXCompanyLoader()
@classmethod
def get_loader(cls):
return cls._loader
class FundamentalsDataSet(DataSet):
"""
:class:`~zipline.pipeline.data.DataSet` containing daily trading prices and
volumes.
"""
ROEAVE3 = Column(float64_dtype)
total_share_0QE = Column(float64_dtype)
ipo_date = Column(float64_dtype)
delist_date = Column(float64_dtype)
IndustryId = Column(float64_dtype)
ipo_date_test = Column(datetime)
industry_id = Column(object_dtype)
class EquityPricing(DataSet):
"""
:class:`~zipline.pipeline.data.DataSet` containing daily trading prices and
volumes.
"""
open = Column(float64_dtype, currency_aware=True)
high = Column(float64_dtype, currency_aware=True)
low = Column(float64_dtype, currency_aware=True)
close = Column(float64_dtype, currency_aware=True)
volume = Column(float64_dtype)
currency = Column(categorical_dtype)
def new_dataset(expr, deltas):
"""Creates or returns a dataset from a pair of blaze expressions.
Parameters
----------
expr : Expr
The blaze expression representing the first known values.
deltas : Expr
The blaze expression representing the deltas to the data.
Returns
-------
ds : type
A new dataset type.
Notes
-----
This function is memoized. repeated calls with the same inputs will return
the same type.
"""
columns = {}
for name, type_ in expr.dshape.measure.fields:
try:
if promote(type_, float64, promote_option=False) != float64:
raise NotPipelineCompatible()
if isinstance(type_, Option):
type_ = type_.ty
except NotPipelineCompatible:
col = NonPipelineField(name, type_)
except TypeError:
col = NonNumpyField(name, type_)
else:
col = Column(type_.to_numpy_dtype())
columns[name] = col
name = expr._name
if name is None:
name = next(_new_names)
# unicode is a name error in py3 but the branch is only hit
# when we are in python 2.
if PY2 and isinstance(name, unicode): # noqa
name = name.encode('utf-8')
return type(name, (DataSet, ), columns)
class IEXKeyStats(DataSet):
'''
"companyName": "Apple Inc.",
"marketcap": 760334287200,
"beta": 1.295227,
"week52high": 156.65,
"week52low": 93.63,
"week52change": 58.801903,
"shortInterest": 55544287,
"shortDate": "2017-06-15",
"dividendRate": 2.52,
"dividendYield": 1.7280395,
"exDividendDate": "2017-05-11 00:00:00.0",
"latestEPS": 8.29,
"latestEPSDate": "2016-09-30",
"sharesOutstanding": 5213840000,
"float": 5203997571, "returnOnEquity": 0.08772939519857577,
"consensusEPS": 3.22,
"numberOfEstimates": 15,
"symbol": "AAPL",
"EBITDA": 73828000000,
"revenue": 220457000000,
"grossProfit": 84686000000,
"cash": 256464000000,
"debt": 358038000000,
"ttmEPS": 8.55,
"revenuePerShare": 42.2830389885382,
"revenuePerEmployee": 1900491.3793103448, "peRatioHigh": 25.5,
"peRatioLow": 8.7,
"EPSSurpriseDollar": null,
"EPSSurprisePercent": 3.9604,
"returnOnAssets": 14.15,
"returnOnCapital": null,
"profitMargin": 20.73,
"priceToSales": 3.6668503,
"priceToBook": 6.19,
"day200MovingAvg": 140.60541,
"day50MovingAvg": 156.49678,
"institutionPercent": 32.1,
"insiderPercent": null,
"shortRatio": 1.6915414,
"year5ChangePercent": 0.5902546932200027,
"year2ChangePercent": 0.3777449874142869,
"year1ChangePercent": 0.39751716851558366,
"ytdChangePercent": 0.36659492036160124,
"month6ChangePercent": 0.12208398133748043,
"month3ChangePercent": 0.08466584665846649,
"month1ChangePercent": 0.009668596145283263,
"day5ChangePercent": -0.005762605699968781
'''
companyName = Column(object_dtype)
marketcap = Column(float64_dtype, missing_value=np.nan)
beta = Column(float64_dtype, missing_value=np.nan)
week52high = Column(float64_dtype, missing_value=np.nan)
week52low = Column(float64_dtype, missing_value=np.nan)
week52change = Column(float64_dtype, missing_value=np.nan)
shortInterest = Column(float64_dtype, missing_value=np.nan)
shortDate = Column(datetime64ns_dtype,
missing_value=np.datetime64('1970-01-01'))
dividendRate = Column(float64_dtype, missing_value=np.nan)
dividendYield = Column(float64_dtype, missing_value=np.nan)
exDividendDate = Column(datetime64ns_dtype,
missing_value=np.datetime64('1970-01-01'))
latestEPS = Column(float64_dtype, missing_value=np.nan)
latestEPSDate = Column(datetime64ns_dtype,
missing_value=np.datetime64('1970-01-01'))
sharesOutstanding = Column(float64_dtype, missing_value=np.nan)
float = Column(float64_dtype, missing_value=np.nan)
returnOnEquity = Column(float64_dtype, missing_value=np.nan)
consensusEPS = Column(float64_dtype, missing_value=np.nan)
numberOfEstimates = Column(float64_dtype, missing_value=np.nan)
symbol = Column(object_dtype)
EBITDA = Column(float64_dtype, missing_value=np.nan)
revenue = Column(float64_dtype, missing_value=np.nan)
grossProfit = Column(float64_dtype, missing_value=np.nan)
cash = Column(float64_dtype, missing_value=np.nan)
debt = Column(float64_dtype, missing_value=np.nan)
ttmEPS = Column(float64_dtype, missing_value=np.nan)
revenuePerShare = Column(float64_dtype, missing_value=np.nan)
revenuePerEmployee = Column(float64_dtype, missing_value=np.nan)
peRatio = Column(float64_dtype, missing_value=np.nan)
EPSSurpriseDollar = Column(float64_dtype, missing_value=np.nan)
EPSSurprisePercent = Column(float64_dtype, missing_value=np.nan)
returnOnAssets = Column(float64_dtype, missing_value=np.nan)
returnOnCapital = Column(float64_dtype, missing_value=np.nan)
profitMargin = Column(float64_dtype, missing_value=np.nan)
priceToSales = Column(float64_dtype, missing_value=np.nan)
priceToBook = Column(float64_dtype, missing_value=np.nan)
day200MovingAvg = Column(float64_dtype, missing_value=np.nan)
day50MovingAvg = Column(float64_dtype, missing_value=np.nan)
institutionPercent = Column(float64_dtype, missing_value=np.nan)
insiderPercent = Column(float64_dtype, missing_value=np.nan)
shortRatio = Column(float64_dtype, missing_value=np.nan)
year5ChangePercent = Column(float64_dtype, missing_value=np.nan)
year2ChangePercent = Column(float64_dtype, missing_value=np.nan)
year1ChangePercent = Column(float64_dtype, missing_value=np.nan)
ytdChangePercent = Column(float64_dtype, missing_value=np.nan)
month6ChangePercent = Column(float64_dtype, missing_value=np.nan)
month3ChangePercent = Column(float64_dtype, missing_value=np.nan)
month1ChangePercent = Column(float64_dtype, missing_value=np.nan)
day5ChangePercent = Column(float64_dtype, missing_value=np.nan)
_loader = IEXKeyStatsLoader()
@classmethod
def get_loader(cls):
return cls._loader
class PolygonCompany(DataSet):
'''
"logo": "https://s3.polygon.io/logos/aapl/logo.png",
"exchange": "Nasdaq Global Select",
"name": "Apple Inc.",
"symbol": "AAPL",
"listdate": "2018-08-15",
"cik": "0000320193",
"bloomberg": "EQ0010169500001000",
"figi": "string",
"lei": "HWUPKR0MPOU8FGXBT394",
"sic": 3571,
"country": "us",
"industry": "Computer Hardware",
"sector": "Technology",
"marketcap": 815604985500,
"employees": 116000,
"phone": "(408) 996-1010",
"ceo": "Tim Cook",
"url": "http://www.apple.com",
"description": "Apple Inc. designs, manufactures, and markets mobile communication and media devices, personal computers, and portable digital music players to consumers...\n",
''' # noqa
exchange = Column(object_dtype, missing_value='')
name = Column(object_dtype, missing_value='')
symbol = Column(object_dtype, missing_value='')
listdate = Column(datetime64D_dtype,
missing_value=np.datetime64('1970-01-01'))
cik = Column(object_dtype, missing_value='')
bloomberg = Column(object_dtype, missing_value='')
figi = Column(object_dtype, missing_value='')
lei = Column(object_dtype, missing_value='')
sic = Column(float64_dtype, missing_value=np.nan)
country = Column(object_dtype, missing_value='')
industry = Column(object_dtype, missing_value='')
sector = Column(object_dtype, missing_value='')
marketcap = Column(float64_dtype, missing_value=np.nan)
employees = Column(float64_dtype, missing_value=np.nan)
phone = Column(object_dtype, missing_value='')
ceo = Column(object_dtype, missing_value='')
tags = Column(object_dtype, missing_value='')
_loader = PolygonCompanyLoader()
@classmethod
def get_loader(cls):
return cls._loader
class LargeDataSet(DataSet):
locals().update(
{name: Column(dtype=float)
for name in string.ascii_lowercase})
class SomeDataSet(DataSet):
a = Column(dtype=float)
b = Column(dtype=object)
c = Column(dtype=int, missing_value=-1)
exists_but_not_a_column = "foo"
class IEXFinancials(DataSet):
'''
"reportDate": "2019-03-31",
"grossProfit": 21648000000,
"costOfRevenue": 36270000000,
"operatingRevenue": 57918000000,
"totalRevenue": 57918000000,
"operatingIncome": 13242000000,
"netIncome": 11561000000,
"researchAndDevelopment": 3948000000,
"operatingExpense": 44676000000,
"currentAssets": 123346000000,
"totalAssets": 341998000000,
"totalLiabilities": 236138000000,
"currentCash": 38329000000,
"currentDebt": 22429000000,
"shortTermDebt": 22429000000,
"longTermDebt": 90201000000,
"totalCash": 80433000000,
"totalDebt": 112630000000,
"shareholderEquity": 105860000000,
"cashChange": -4954000000,
"cashFlow": 11155000000
'''
reportDate = Column(float64_dtype, missing_value=np.nan)
grossProfit = Column(float64_dtype, missing_value=np.nan)
costOfRevenue = Column(float64_dtype, missing_value=np.nan)
operatingRevenue = Column(float64_dtype, missing_value=np.nan)
totalRevenue = Column(float64_dtype, missing_value=np.nan)
operatingIncome = Column(float64_dtype, missing_value=np.nan)
netIncome = Column(float64_dtype, missing_value=np.nan)
researchAndDevelopment = Column(float64_dtype, missing_value=np.nan)
operatingExpense = Column(float64_dtype, missing_value=np.nan)
currentAssets = Column(float64_dtype, missing_value=np.nan)
totalAssets = Column(float64_dtype, missing_value=np.nan)
totalLiabilities = Column(float64_dtype, missing_value=np.nan)
currentCash = Column(float64_dtype, missing_value=np.nan)
currentDebt = Column(float64_dtype, missing_value=np.nan)
shortTermDebt = Column(float64_dtype, missing_value=np.nan)
longTermDebt = Column(float64_dtype, missing_value=np.nan)
totalCash = Column(float64_dtype, missing_value=np.nan)
totalDebt = Column(float64_dtype, missing_value=np.nan)
shareholderEquity = Column(float64_dtype, missing_value=np.nan)
cashChange = Column(float64_dtype, missing_value=np.nan)
cashFlow = Column(float64_dtype, missing_value=np.nan)
_loader = IEXFinancialsLoader()
@classmethod
def get_loader(cls):
return cls._loader
class LargeDataSet(DataSet):
locals().update({
name: Column(dtype=float)
for name in chrange('a', 'z')
})
class SomeDataSet(DataSet):
a = Column(dtype=float)
b = Column(dtype=object)
c = Column(dtype=int, missing_value=-1)
from zipline.utils.numpy_utils import float64_dtype, categorical_dtype
from zipline.pipeline.domain import US_EQUITIES
from zipline.pipeline.data.dataset import Column, DataSet
MyDataSet = type(
"MyDataset",
(DataSet, ),
{
x: Column(float64_dtype)
for x in [
'open',
'high',
'low',
'close',
'volume',
# 'ex_dividend',
# 'split_ratio',
# 'adj_open',
# 'adj_high',
# 'adj_low',
# 'adj_close',
# 'adj_volume'
]
})
if __name__ == '__main__':
dat = MyDataSet
