def create_adjustments_reader(cls):
path = cls.tempdir.getpath("test_adjustments.db")
adj_writer = SQLiteAdjustmentWriter(
path,
cls.env.trading_days,
MockDailyBarReader()
)
splits = pd.DataFrame([
{
'effective_date': str_to_seconds("2016-01-06"),
'ratio': 0.5,
'sid': cls.asset3.sid
}
])
# Mergers and Dividends are not tested, but we need to have these
# anyway
mergers = pd.DataFrame({}, columns=['effective_date', 'ratio', 'sid'])
mergers.effective_date = mergers.effective_date.astype(np.int64)
mergers.ratio = mergers.ratio.astype(np.float64)
mergers.sid = mergers.sid.astype(np.int64)
dividends = pd.DataFrame({}, columns=['ex_date', 'record_date',
'declared_date', 'pay_date',
'amount', 'sid'])
dividends.amount = dividends.amount.astype(np.float64)
dividends.sid = dividends.sid.astype(np.int64)
adj_writer.write(splits, mergers, dividends)
return SQLiteAdjustmentReader(path)
def create_adjustment_reader(cls, tempdir):
dbpath = tempdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
MockDailyBarSpotReader())
splits = DataFrame.from_records([
{
'effective_date': str_to_seconds('2014-06-09'),
'ratio': (1 / 7.0),
'sid': cls.AAPL,
}
])
mergers = DataFrame(
{
# Hackery to make the dtypes correct on an empty frame.
'effective_date': array([], dtype=int),
'ratio': array([], dtype=float),
'sid': array([], dtype=int),
},
index=DatetimeIndex([]),
columns=['effective_date', 'ratio', 'sid'],
)
dividends = DataFrame({
'sid': array([], dtype=uint32),
'amount': array([], dtype=float64),
'record_date': array([], dtype='datetime64[ns]'),
'ex_date': array([], dtype='datetime64[ns]'),
'declared_date': array([], dtype='datetime64[ns]'),
'pay_date': array([], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
return SQLiteAdjustmentReader(dbpath)
def __init__(self):
conn = sqlite3_connect(":memory:")
writer = SQLiteAdjustmentWriter(conn)
empty = DataFrame(
{"sid": array([], dtype=uint32), "effective_date": array([], dtype=uint32), "ratio": array([], dtype=float)}
)
writer.write(splits=empty, mergers=empty, dividends=empty)
super(NullAdjustmentReader, self).__init__(conn)
def setUpClass(cls):
cls.test_data_dir = TempDirectory()
cls.db_path = cls.test_data_dir.getpath("adjustments.db")
all_days = TradingEnvironment().trading_days
cls.calendar_days = all_days[all_days.slice_indexer(TEST_CALENDAR_START, TEST_CALENDAR_STOP)]
daily_bar_reader = MockDailyBarSpotReader()
writer = SQLiteAdjustmentWriter(cls.db_path, cls.calendar_days, daily_bar_reader)
writer.write(SPLITS, MERGERS, DIVIDENDS)
cls.assets = TEST_QUERY_ASSETS
cls.asset_info = EQUITY_INFO
cls.bcolz_writer = SyntheticDailyBarWriter(cls.asset_info, cls.calendar_days)
cls.bcolz_path = cls.test_data_dir.getpath("equity_pricing.bcolz")
cls.bcolz_writer.write(cls.bcolz_path, cls.calendar_days, cls.assets)
def __init__(self):
conn = sqlite3_connect(':memory:')
writer = SQLiteAdjustmentWriter(conn, None, None)
empty = DataFrame({
'sid': array([], dtype=uint32),
'effective_date': array([], dtype=uint32),
'ratio': array([], dtype=float),
})
empty_dividends = DataFrame({
'sid': array([], dtype=uint32),
'amount': array([], dtype=float64),
'record_date': array([], dtype='datetime64[ns]'),
'ex_date': array([], dtype='datetime64[ns]'),
'declared_date': array([], dtype='datetime64[ns]'),
'pay_date': array([], dtype='datetime64[ns]'),
})
writer.write(splits=empty, mergers=empty, dividends=empty_dividends)
super(NullAdjustmentReader, self).__init__(conn)
def __init__(self):
conn = sqlite3_connect(":memory:")
writer = SQLiteAdjustmentWriter(conn, None, None)
empty = DataFrame(
{"sid": array([], dtype=uint32), "effective_date": array([], dtype=uint32), "ratio": array([], dtype=float)}
)
empty_dividends = DataFrame(
{
"sid": array([], dtype=uint32),
"amount": array([], dtype=float64),
"record_date": array([], dtype="datetime64[ns]"),
"ex_date": array([], dtype="datetime64[ns]"),
"declared_date": array([], dtype="datetime64[ns]"),
"pay_date": array([], dtype="datetime64[ns]"),
}
)
writer.write(splits=empty, mergers=empty, dividends=empty_dividends)
super(NullAdjustmentReader, self).__init__(conn)
def setUpClass(cls):
cls.test_data_dir = TempDirectory()
cls.db_path = cls.test_data_dir.getpath('adjustments.db')
all_days = TradingEnvironment().trading_days
cls.calendar_days = all_days[all_days.slice_indexer(
TEST_CALENDAR_START, TEST_CALENDAR_STOP)]
daily_bar_reader = MockDailyBarSpotReader()
writer = SQLiteAdjustmentWriter(cls.db_path, cls.calendar_days,
daily_bar_reader)
writer.write(SPLITS, MERGERS, DIVIDENDS)
cls.assets = TEST_QUERY_ASSETS
cls.asset_info = EQUITY_INFO
cls.bcolz_writer = SyntheticDailyBarWriter(
cls.asset_info,
cls.calendar_days,
)
cls.bcolz_path = cls.test_data_dir.getpath('equity_pricing.bcolz')
cls.bcolz_writer.write(cls.bcolz_path, cls.calendar_days, cls.assets)
def create_mock_adjustments(tempdir, days, splits=None, dividends=None,
mergers=None):
path = tempdir.getpath("test_adjustments.db")
# create a split for the last day
writer = SQLiteAdjustmentWriter(path, days, MockDailyBarReader())
if splits is None:
splits = create_empty_splits_mergers_frame()
else:
splits = pd.DataFrame(splits)
if mergers is None:
mergers = create_empty_splits_mergers_frame()
else:
mergers = pd.DataFrame(mergers)
if dividends is None:
data = {
# Hackery to make the dtypes correct on an empty frame.
'ex_date': np.array([], dtype='datetime64[ns]'),
'pay_date': np.array([], dtype='datetime64[ns]'),
'record_date': np.array([], dtype='datetime64[ns]'),
'declared_date': np.array([], dtype='datetime64[ns]'),
'amount': np.array([], dtype=float),
'sid': np.array([], dtype=int),
}
dividends = pd.DataFrame(
data,
index=pd.DatetimeIndex([], tz='UTC'),
columns=['ex_date',
'pay_date',
'record_date',
'declared_date',
'amount',
'sid']
)
else:
if not isinstance(dividends, pd.DataFrame):
dividends = pd.DataFrame(dividends)
writer.write(splits, mergers, dividends)
return path
def create_adjustment_reader(cls, tempdir):
dbpath = tempdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
MockDailyBarSpotReader())
splits = DataFrame.from_records([
{
'effective_date': str_to_seconds('2014-06-09'),
'ratio': (1 / 7.0),
'sid': cls.AAPL,
}
])
mergers = create_empty_splits_mergers_frame()
dividends = DataFrame({
'sid': array([], dtype=uint32),
'amount': array([], dtype=float64),
'record_date': array([], dtype='datetime64[ns]'),
'ex_date': array([], dtype='datetime64[ns]'),
'declared_date': array([], dtype='datetime64[ns]'),
'pay_date': array([], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
return SQLiteAdjustmentReader(dbpath)
def create_adjustment_reader(cls, tempdir):
dbpath = tempdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(dbpath)
splits = DataFrame.from_records([{
'effective_date':
str_to_seconds('2014-06-09'),
'ratio': (1 / 7.0),
'sid':
cls.AAPL,
}])
mergers = dividends = DataFrame(
{
# Hackery to make the dtypes correct on an empty frame.
'effective_date': array([], dtype=int),
'ratio': array([], dtype=float),
'sid': array([], dtype=int),
},
index=DatetimeIndex([], tz='UTC'),
columns=['effective_date', 'ratio', 'sid'],
)
writer.write(splits, mergers, dividends)
return SQLiteAdjustmentReader(dbpath)
def create_adjustment_reader(cls, tempdir):
dbpath = tempdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(dbpath)
splits = DataFrame.from_records([
{
'effective_date': str_to_seconds('2014-06-09'),
'ratio': (1 / 7.0),
'sid': cls.AAPL,
}
])
mergers = dividends = DataFrame(
{
# Hackery to make the dtypes correct on an empty frame.
'effective_date': array([], dtype=int),
'ratio': array([], dtype=float),
'sid': array([], dtype=int),
},
index=DatetimeIndex([], tz='UTC'),
columns=['effective_date', 'ratio', 'sid'],
)
writer.write(splits, mergers, dividends)
return SQLiteAdjustmentReader(dbpath)
def create_adjustment_reader(cls, tempdir):
dbpath = tempdir.getpath('adjustments.sqlite')
writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
MockDailyBarSpotReader())
splits = DataFrame.from_records([{
'effective_date':
str_to_seconds('2014-06-09'),
'ratio': (1 / 7.0),
'sid':
cls.AAPL,
}])
mergers = DataFrame(
{
# Hackery to make the dtypes correct on an empty frame.
'effective_date': array([], dtype=int),
'ratio': array([], dtype=float),
'sid': array([], dtype=int),
},
index=DatetimeIndex([], tz='UTC'),
columns=['effective_date', 'ratio', 'sid'],
)
dividends = DataFrame({
'sid':
array([], dtype=uint32),
'amount':
array([], dtype=float64),
'record_date':
array([], dtype='datetime64[ns]'),
'ex_date':
array([], dtype='datetime64[ns]'),
'declared_date':
array([], dtype='datetime64[ns]'),
'pay_date':
array([], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends)
return SQLiteAdjustmentReader(dbpath)
def __init__(self):
conn = sqlite3_connect(':memory:')
writer = SQLiteAdjustmentWriter(conn, None, None)
empty = DataFrame({
'sid': array([], dtype=uint32),
'effective_date': array([], dtype=uint32),
'ratio': array([], dtype=float),
})
empty_dividends = DataFrame({
'sid':
array([], dtype=uint32),
'amount':
array([], dtype=float64),
'record_date':
array([], dtype='datetime64[ns]'),
'ex_date':
array([], dtype='datetime64[ns]'),
'declared_date':
array([], dtype='datetime64[ns]'),
'pay_date':
array([], dtype='datetime64[ns]'),
})
writer.write(splits=empty, mergers=empty, dividends=empty_dividends)
super(NullAdjustmentReader, self).__init__(conn)
def setUpClass(cls):
cls.env = TradingEnvironment()
cls.tempdir = TempDirectory()
cls.sim_params = factory.create_simulation_parameters()
cls.env.write_data(equities_data={
1: {
"start_date": cls.sim_params.trading_days[0],
"end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
},
2: {
"start_date": cls.sim_params.trading_days[0],
"end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
},
3: {
"start_date": cls.sim_params.trading_days[100],
"end_date": cls.sim_params.trading_days[-100]
},
4: {
"start_date": cls.sim_params.trading_days[0],
"end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
}
})
dbpath = os.path.join(cls.tempdir.path, "adjustments.db")
writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
MockDailyBarSpotReader())
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid': np.array([], dtype=np.uint32),
'amount': np.array([], dtype=np.float64),
'declared_date': np.array([], dtype='datetime64[ns]'),
'ex_date': np.array([], dtype='datetime64[ns]'),
'pay_date': np.array([], dtype='datetime64[ns]'),
'record_date': np.array([], dtype='datetime64[ns]'),
})
declared_date = cls.sim_params.trading_days[45]
ex_date = cls.sim_params.trading_days[50]
record_date = pay_date = cls.sim_params.trading_days[55]
stock_dividends = pd.DataFrame({
'sid': np.array([4], dtype=np.uint32),
'payment_sid': np.array([5], dtype=np.uint32),
'ratio': np.array([2], dtype=np.float64),
'declared_date': np.array([declared_date], dtype='datetime64[ns]'),
'ex_date': np.array([ex_date], dtype='datetime64[ns]'),
'record_date': np.array([record_date], dtype='datetime64[ns]'),
'pay_date': np.array([pay_date], dtype='datetime64[ns]'),
})
writer.write(splits, mergers, dividends,
stock_dividends=stock_dividends)
cls.data_portal = create_data_portal(
cls.env,
cls.tempdir,
cls.sim_params,
[1, 2, 3, 4],
adjustment_reader=SQLiteAdjustmentReader(dbpath)
)
def create_adjustments_reader(cls):
path = cls.tempdir.getpath("test_adjustments.db")
adj_writer = SQLiteAdjustmentWriter(
path,
cls.env.trading_days,
MockDailyBarReader()
)
splits = pd.DataFrame([
{
'effective_date': str_to_seconds("2016-01-06"),
'ratio': 0.5,
'sid': cls.SPLIT_ASSET.sid
},
{
'effective_date': str_to_seconds("2016-01-07"),
'ratio': 0.5,
'sid': cls.ILLIQUID_SPLIT_ASSET.sid
}
])
mergers = pd.DataFrame([
{
'effective_date': str_to_seconds("2016-01-06"),
'ratio': 0.5,
'sid': cls.MERGER_ASSET.sid
},
{
'effective_date': str_to_seconds("2016-01-07"),
'ratio': 0.6,
'sid': cls.ILLIQUID_MERGER_ASSET.sid
}
])
# we're using a fake daily reader in the adjustments writer which
# returns every daily price as 100, so dividend amounts of 2.0 and 4.0
# correspond to 2% and 4% dividends, respectively.
dividends = pd.DataFrame([
{
# only care about ex date, the other dates don't matter here
'ex_date':
pd.Timestamp("2016-01-06", tz='UTC').to_datetime64(),
'record_date':
pd.Timestamp("2016-01-06", tz='UTC').to_datetime64(),
'declared_date':
pd.Timestamp("2016-01-06", tz='UTC').to_datetime64(),
'pay_date':
pd.Timestamp("2016-01-06", tz='UTC').to_datetime64(),
'amount': 2.0,
'sid': cls.DIVIDEND_ASSET.sid
},
{
'ex_date':
pd.Timestamp("2016-01-07", tz='UTC').to_datetime64(),
'record_date':
pd.Timestamp("2016-01-07", tz='UTC').to_datetime64(),
'declared_date':
pd.Timestamp("2016-01-07", tz='UTC').to_datetime64(),
'pay_date':
pd.Timestamp("2016-01-07", tz='UTC').to_datetime64(),
'amount': 4.0,
'sid': cls.ILLIQUID_DIVIDEND_ASSET.sid
}],
columns=['ex_date',
'record_date',
'declared_date',
'pay_date',
'amount',
'sid']
)
adj_writer.write(splits, mergers, dividends)
return SQLiteAdjustmentReader(path)
def create_adjustments_reader(cls):
path = cls.tempdir.getpath("test_adjustments.db")
adj_writer = SQLiteAdjustmentWriter(path, cls.env.trading_days,
MockDailyBarReader())
splits = pd.DataFrame([{
'effective_date': str_to_seconds("2016-01-06"),
'ratio': 0.5,
'sid': cls.SPLIT_ASSET.sid
}, {
'effective_date': str_to_seconds("2016-01-07"),
'ratio': 0.5,
'sid': cls.ILLIQUID_SPLIT_ASSET.sid
}])
mergers = pd.DataFrame([{
'effective_date': str_to_seconds("2016-01-06"),
'ratio': 0.5,
'sid': cls.MERGER_ASSET.sid
}, {
'effective_date': str_to_seconds("2016-01-07"),
'ratio': 0.6,
'sid': cls.ILLIQUID_MERGER_ASSET.sid
}])
# we're using a fake daily reader in the adjustments writer which
# returns every daily price as 100, so dividend amounts of 2.0 and 4.0
# correspond to 2% and 4% dividends, respectively.
dividends = pd.DataFrame(
[
{
# only care about ex date, the other dates don't matter here
'ex_date':
pd.Timestamp("2016-01-06", tz='UTC').to_datetime64(),
'record_date':
pd.Timestamp("2016-01-06", tz='UTC').to_datetime64(),
'declared_date':
pd.Timestamp("2016-01-06", tz='UTC').to_datetime64(),
'pay_date':
pd.Timestamp("2016-01-06", tz='UTC').to_datetime64(),
'amount':
2.0,
'sid':
cls.DIVIDEND_ASSET.sid
},
{
'ex_date':
pd.Timestamp("2016-01-07", tz='UTC').to_datetime64(),
'record_date':
pd.Timestamp("2016-01-07", tz='UTC').to_datetime64(),
'declared_date':
pd.Timestamp("2016-01-07", tz='UTC').to_datetime64(),
'pay_date':
pd.Timestamp("2016-01-07", tz='UTC').to_datetime64(),
'amount':
4.0,
'sid':
cls.ILLIQUID_DIVIDEND_ASSET.sid
}
],
columns=[
'ex_date', 'record_date', 'declared_date', 'pay_date',
'amount', 'sid'
])
adj_writer.write(splits, mergers, dividends)
return SQLiteAdjustmentReader(path)
def setUpClass(cls):
cls.env = TradingEnvironment()
cls.tempdir = TempDirectory()
cls.sim_params = factory.create_simulation_parameters()
cls.env.write_data(
equities_data={
1: {
"start_date": cls.sim_params.trading_days[0],
"end_date": cls.sim_params.trading_days[-1] +
timedelta(days=1)
},
2: {
"start_date": cls.sim_params.trading_days[0],
"end_date": cls.sim_params.trading_days[-1] +
timedelta(days=1)
},
3: {
"start_date": cls.sim_params.trading_days[100],
"end_date": cls.sim_params.trading_days[-100]
},
4: {
"start_date": cls.sim_params.trading_days[0],
"end_date": cls.sim_params.trading_days[-1] +
timedelta(days=1)
}
})
dbpath = os.path.join(cls.tempdir.path, "adjustments.db")
writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
MockDailyBarSpotReader())
splits = mergers = create_empty_splits_mergers_frame()
dividends = pd.DataFrame({
'sid':
np.array([], dtype=np.uint32),
'amount':
np.array([], dtype=np.float64),
'declared_date':
np.array([], dtype='datetime64[ns]'),
'ex_date':
np.array([], dtype='datetime64[ns]'),
'pay_date':
np.array([], dtype='datetime64[ns]'),
'record_date':
np.array([], dtype='datetime64[ns]'),
})
declared_date = cls.sim_params.trading_days[45]
ex_date = cls.sim_params.trading_days[50]
record_date = pay_date = cls.sim_params.trading_days[55]
stock_dividends = pd.DataFrame({
'sid':
np.array([4], dtype=np.uint32),
'payment_sid':
np.array([5], dtype=np.uint32),
'ratio':
np.array([2], dtype=np.float64),
'declared_date':
np.array([declared_date], dtype='datetime64[ns]'),
'ex_date':
np.array([ex_date], dtype='datetime64[ns]'),
'record_date':
np.array([record_date], dtype='datetime64[ns]'),
'pay_date':
np.array([pay_date], dtype='datetime64[ns]'),
})
writer.write(splits,
mergers,
dividends,
stock_dividends=stock_dividends)
cls.data_portal = create_data_portal(
cls.env,
cls.tempdir,
cls.sim_params, [1, 2, 3, 4],
adjustment_reader=SQLiteAdjustmentReader(dbpath))
