def test_roll_to_roll_two_generics():
contract_dates = pd.Series([
TS('2016-10-10'),
TS('2016-10-13'),
TS('2016-10-17'),
TS('2016-10-20')
],
index=['A', 'B', 'C', 'D'])
timestamps = pd.date_range(contract_dates.iloc[0] + BDay(-2),
contract_dates.iloc[1],
freq='b')
cols = pd.MultiIndex.from_product([[0, 1], ['front', 'back']])
idx = [-2, -1, 0]
trans = pd.DataFrame([[1, 0, 1, 0], [0.5, 0.5, 0.5, 0.5], [0, 1, 0, 1]],
index=idx,
columns=cols)
wts = mappings.roller(timestamps,
contract_dates,
mappings.static_transition,
transition=trans)
midx = pd.MultiIndex.from_tuples([
(timestamps[0], 'A'), (timestamps[0], 'B'), (timestamps[1], 'A'),
(timestamps[1], 'B'), (timestamps[1], 'C'), (timestamps[2], 'B'),
(timestamps[2], 'C'), (timestamps[3], 'B'), (timestamps[3], 'C'),
(timestamps[4], 'B'), (timestamps[4], 'C'), (timestamps[4], 'D'),
(timestamps[5], 'C'), (timestamps[5], 'D')
])
midx.names = ['date', 'contract']
cols = pd.Index([0, 1], name='generic')
vals = [[1, 0], [0, 1], [0.5, 0], [0.5, 0.5], [0, 0.5], [1, 0], [0, 1],
[1, 0], [0, 1], [0.5, 0], [0.5, 0.5], [0, 0.5], [1, 0], [0, 1]]
wts_exp = pd.DataFrame(vals, index=midx, columns=cols)
assert_frame_equal(wts, wts_exp)
def test_read_price_data(price_files):
# using default name_func in read_price_data()
df = util.read_price_data(price_files)
dt1 = TS("2014-09-30")
dt2 = TS("2014-10-01")
idx = pd.MultiIndex.from_tuples([(dt1, "CME-FVU2014"),
(dt1, "CME-FVZ2014"),
(dt2, "CME-FVZ2014")],
names=["date", "contract"])
df_exp = pd.DataFrame([119.27344, 118.35938, 118.35938],
index=idx,
columns=["Open"])
assert_frame_equal(df, df_exp)
def name_func(fstr):
file_name = os.path.split(fstr)[-1]
name = file_name.split('-')[1].split('.')[0]
return name[-4:] + name[:3]
df = util.read_price_data(price_files, name_func)
dt1 = TS("2014-09-30")
dt2 = TS("2014-10-01")
idx = pd.MultiIndex.from_tuples([(dt1, "2014FVU"), (dt1, "2014FVZ"),
(dt2, "2014FVZ")],
names=["date", "contract"])
df_exp = pd.DataFrame([119.27344, 118.35938, 118.35938],
index=idx,
columns=["Open"])
assert_frame_equal(df, df_exp)
def test_unflatten_dict():
flat_wts = pd.DataFrame({
"date":
([TS('2015-01-03')] * 4 + [TS('2015-01-04')] * 4 + [TS('2015-01-03')]),
"contract": (['CLF5'] * 2 + ['CLG5'] * 4 + ['CLH5'] * 2 + ["COF5"]),
"generic": ["CL1", "CL2"] * 4 + ["CO1"],
"weight": [1, 0, 0, 1, 1, 0, 0, 1, 1],
"key": ["CL"] * 8 + ["CO"]
}).loc[:, ["date", "contract", "generic", "weight", "key"]]
wts = util.unflatten(flat_wts)
vals = [[1, 0], [0, 1], [1, 0], [0, 1]]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5')],
names=("date", "contract"))
cols = pd.Index(["CL1", "CL2"], name="generic")
weights1 = pd.DataFrame(vals, index=widx, columns=cols)
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'COF5')],
names=("date", "contract"))
cols = pd.Index(["CO1"], name="generic")
weights2 = pd.DataFrame(1, index=widx, columns=cols)
wts_exp = {"CL": weights1, "CO": weights2}
assert_dict_of_frames(wts, wts_exp)
def test_bdom():
exp_cols = ["date", "year", "month", "bdom", "month_code"]
months = {1: "G", 3: "J", 8: "U"}
date_info = mappings.bdom_roll_date("20160115", "20171231", 2, months)
date_info_exp = pd.DataFrame({
"date": [
TS("20160302"),
TS("20160802"),
TS("20170103"),
TS("20170302"),
TS("20170802")
],
"year": [2016, 2016, 2017, 2017, 2017],
"month": [3, 8, 1, 3, 8],
"bdom":
2,
"month_code": ["J", "U", "G", "J", "U"]
})
date_info_exp = date_info_exp.loc[:, exp_cols]
assert_frame_equal(date_info, date_info_exp)
# with holidays
months = {1: "G", 3: "J"}
date_info = mappings.bdom_roll_date("20160115",
"20171231",
3,
months,
holidays=[pd.Timestamp("20160301")])
date_info_exp = pd.DataFrame({
"date": [TS("20160304"),
TS("20170104"),
TS("20170303")],
"year": [2016, 2017, 2017],
"month": [3, 1, 3],
"bdom":
3,
"month_code": ["J", "G", "J"]
})
date_info_exp = date_info_exp.loc[:, exp_cols]
assert_frame_equal(date_info, date_info_exp)
date_info_empty = mappings.bdom_roll_date("20160101", "20150101", 2,
months)
date_info_exp = pd.DataFrame(index=range(0), columns=exp_cols)
assert_frame_equal(date_info_empty, date_info_exp, check_dtype=False)
# forget to pass bdom, check for type error
holidays = [pd.Timestamp("20160301")]
with pytest.raises(ValueError):
mappings.bdom_roll_date("20160115", "20171231", months, holidays)
def test_calc_rets_missing_instr_rets_key_error():
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5')])
irets = pd.Series([0.02, 0.01, 0.012], index=idx)
vals = [1, 1 / 2, 1 / 2, 1]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-05'), 'CLG5')])
weights = pd.DataFrame(vals, index=widx, columns=["CL1"])
with pytest.raises(KeyError):
util.calc_rets(irets, weights)
def test_calc_rets_one_generic():
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-05'), 'CLG5')])
rets = pd.Series([0.1, 0.05, 0.1, 0.8], index=idx)
vals = [1, 0.5, 0.5, 1]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-05'), 'CLG5')])
weights = pd.DataFrame(vals, index=widx, columns=['CL1'])
wrets = util.calc_rets(rets, weights)
wrets_exp = pd.DataFrame([0.1, 0.075, 0.8],
index=weights.index.levels[0],
columns=['CL1'])
assert_frame_equal(wrets, wrets_exp)
def get_data(self,
start_str: str,
stop_str: str,
fetch: bool = True,
fill: bool = False) -> pd.Series:
if not fetch:
return self.cache.iloc[(self.cache.index >= TS(start_str))
& (self.cache.index < TS(stop_str))]
# date_range includes the last item but everything else doesn't
requested_dates: Sequence[TS] = pd.date_range(start_str, stop_str)[:-1]
needed_dates = sorted(
list(set(requested_dates) - set(self.cache.index)))
needed_ranges = find_ranges(needed_dates)
def collect_data(date_range: Tuple[Date, Date]) -> pd.DataFrame:
# why is this a nested function?
str_range: Tuple[str, str] = tuple(map(Date.isoformat, date_range))
raw_data = self.fetch_raw_data_for_range(str_range)
new_data = parsed_data(raw_data) # add nap filtering here
return new_data
collected_df = pd.concat(map(collect_data, needed_ranges))
breakpoint()
if fill:
missing_dates = set(needed_dates) - set(collected_df.index)
row_fill = [None] * len(HEADER[1:])
matrix_fill = [([date] + row_fill) for date in missing_dates]
df_fill = pd.DataFrame(matrix_fill,
columns=HEADER,
index=missing_dates)
collected_df = pd.concat([collected_df, df_fill])
self.cache = pd.concat([self.cache, collected_df],
sort=False).sort_index()
self.cache.name = "dates"
self.cache.to_csv(self.fname, index=True)
return self.cache[requested_dates[0]:requested_dates[-1]]
def test_reindex_with_index_names_smoketest():
idx = pd.MultiIndex.from_tuples([(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-02'), 'CLH5'),
(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLH5')])
prices = pd.Series([103, 101, 102, 100], index=idx)
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLH5')],
names=["date", "contract"])
util.reindex(prices, widx, limit=0)
def test_flatten_dict():
vals = [[1, 0], [0, 1], [1, 0], [0, 1]]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5')])
weights1 = pd.DataFrame(vals, index=widx, columns=["CL1", "CL2"])
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'COF5')])
weights2 = pd.DataFrame(1, index=widx, columns=["CO1"])
weights = {"CL": weights1, "CO": weights2}
flat_wts = util.flatten(weights)
flat_wts_exp = pd.DataFrame({
"date":
([TS('2015-01-03')] * 4 + [TS('2015-01-04')] * 4 + [TS('2015-01-03')]),
"contract": (['CLF5'] * 2 + ['CLG5'] * 4 + ['CLH5'] * 2 + ["COF5"]),
"generic": ["CL1", "CL2"] * 4 + ["CO1"],
"weight": [1, 0, 0, 1, 1, 0, 0, 1, 1],
"key": ["CL"] * 8 + ["CO"]
}).loc[:, ["date", "contract", "generic", "weight", "key"]]
assert_frame_equal(flat_wts, flat_wts_exp)
def test_flatten():
vals = [[1, 0], [0, 1], [1, 0], [0, 1]]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5')])
weights = pd.DataFrame(vals, index=widx, columns=["CL1", "CL2"])
flat_wts = util.flatten(weights)
flat_wts_exp = pd.DataFrame({
"date": [TS('2015-01-03')] * 4 + [TS('2015-01-04')] * 4,
"contract": ['CLF5'] * 2 + ['CLG5'] * 4 + ['CLH5'] * 2,
"generic": ["CL1", "CL2"] * 4,
"weight": [1, 0, 0, 1, 1, 0, 0, 1]
}).loc[:, ["date", "contract", "generic", "weight"]]
assert_frame_equal(flat_wts, flat_wts_exp)
def test_unflatten():
flat_wts = pd.DataFrame({
"date": [TS('2015-01-03')] * 4 + [TS('2015-01-04')] * 4,
"contract": ['CLF5'] * 2 + ['CLG5'] * 4 + ['CLH5'] * 2,
"generic": ["CL1", "CL2"] * 4,
"weight": [1, 0, 0, 1, 1, 0, 0, 1]
}).loc[:, ["date", "contract", "generic", "weight"]]
wts = util.unflatten(flat_wts)
vals = [[1, 0], [0, 1], [1, 0], [0, 1]]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5')],
names=("date", "contract"))
cols = pd.Index(["CL1", "CL2"], name="generic")
wts_exp = pd.DataFrame(vals, index=widx, columns=cols)
assert_frame_equal(wts, wts_exp)
def test_weighted_expiration_two_generics():
vals = [[1, 0, 1 / 2, 1 / 2, 0, 1, 0], [0, 1, 0, 1 / 2, 1 / 2, 0, 1]]
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF15'),
(TS('2015-01-03'), 'CLG15'),
(TS('2015-01-04'), 'CLF15'),
(TS('2015-01-04'), 'CLG15'),
(TS('2015-01-04'), 'CLH15'),
(TS('2015-01-05'), 'CLG15'),
(TS('2015-01-05'), 'CLH15')])
weights = pd.DataFrame({"CL1": vals[0], "CL2": vals[1]}, index=idx)
contract_dates = pd.Series(
[TS('2015-01-20'),
TS('2015-02-21'),
TS('2015-03-20')],
index=['CLF15', 'CLG15', 'CLH15'])
wexp = util.weighted_expiration(weights, contract_dates)
exp_wexp = pd.DataFrame(
[[17.0, 49.0], [32.0, 61.5], [47.0, 74.0]],
index=[TS('2015-01-03'),
TS('2015-01-04'),
TS('2015-01-05')],
columns=["CL1", "CL2"])
assert_frame_equal(wexp, exp_wexp)
def test_calc_rets_two_generics_two_asts():
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5'),
(TS('2015-01-05'), 'CLG5'),
(TS('2015-01-05'), 'CLH5')])
rets1 = pd.Series([0.1, 0.15, 0.05, 0.1, 0.8, -0.5, 0.2], index=idx)
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'COF5'),
(TS('2015-01-03'), 'COG5'),
(TS('2015-01-04'), 'COF5'),
(TS('2015-01-04'), 'COG5'),
(TS('2015-01-04'), 'COH5')])
rets2 = pd.Series([0.1, 0.15, 0.05, 0.1, 0.4], index=idx)
rets = {"CL": rets1, "CO": rets2}
vals = [[1, 0], [0, 1], [0.5, 0], [0.5, 0.5], [0, 0.5], [1, 0], [0, 1]]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5'),
(TS('2015-01-05'), 'CLG5'),
(TS('2015-01-05'), 'CLH5')])
weights1 = pd.DataFrame(vals, index=widx, columns=["CL0", "CL1"])
vals = [[1, 0], [0, 1], [0.5, 0], [0.5, 0.5], [0, 0.5]]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'COF5'),
(TS('2015-01-03'), 'COG5'),
(TS('2015-01-04'), 'COF5'),
(TS('2015-01-04'), 'COG5'),
(TS('2015-01-04'), 'COH5')])
weights2 = pd.DataFrame(vals, index=widx, columns=["CO0", "CO1"])
weights = {"CL": weights1, "CO": weights2}
wrets = util.calc_rets(rets, weights)
wrets_exp = pd.DataFrame(
[[0.1, 0.15, 0.1, 0.15], [0.075, 0.45, 0.075, 0.25],
[-0.5, 0.2, pd.np.NaN, pd.np.NaN]],
index=weights["CL"].index.levels[0],
columns=['CL0', 'CL1', 'CO0', 'CO1'])
assert_frame_equal(wrets, wrets_exp)
def test_calc_rets_two_generics_non_unique_columns():
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5'),
(TS('2015-01-05'), 'CLG5'),
(TS('2015-01-05'), 'CLH5')])
rets = pd.Series([0.1, 0.15, 0.05, 0.1, 0.8, -0.5, 0.2], index=idx)
vals = [[1, 0], [0, 1], [0.5, 0], [0.5, 0.5], [0, 0.5], [1, 0], [0, 1]]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5'),
(TS('2015-01-05'), 'CLG5'),
(TS('2015-01-05'), 'CLH5')])
weights = pd.DataFrame(vals, index=widx, columns=['CL1', 'CL1'])
with pytest.raises(ValueError):
util.calc_rets(rets, weights)
def start_ts_parsed_from_raw_data(raw_data: RawDateData) -> TS:
if isinstance(raw_data["startTime"], str):
return TS(raw_data["startTime"])
raise Exception("malformed data")
def test_reindex():
# related to https://github.com/matthewgilbert/mapping/issues/11
# no op
idx = pd.MultiIndex.from_tuples([(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-02'), 'CLH5'),
(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLH5')])
prices = pd.Series([103, 101, 102, 100], index=idx)
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLH5')])
new_prices = util.reindex(prices, widx, limit=0)
exp_prices = prices
assert_series_equal(exp_prices, new_prices)
# missing front prices error
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5')])
prices = pd.Series([100], index=idx)
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5')])
with pytest.raises(ValueError):
util.reindex(prices, widx, 0)
# NaN returns introduced and filled
idx = pd.MultiIndex.from_tuples([(TS('2015-01-01'), 'CLF5'),
(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-02'), 'CLH5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLH5')])
prices = pd.Series([100, 101, 102, 103, 104], index=idx)
widx = pd.MultiIndex.from_tuples([(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-02'), 'CLH5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLH5'),
(TS('2015-01-05'), 'CLF5'),
(TS('2015-01-05'), 'CLH5'),
(TS('2015-01-06'), 'CLF5'),
(TS('2015-01-06'), 'CLH5')])
new_prices = util.reindex(prices, widx, limit=1)
idx = pd.MultiIndex.from_tuples([(TS('2015-01-01'), 'CLF5'),
(TS('2015-01-01'), 'CLH5'),
(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-02'), 'CLH5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLH5'),
(TS('2015-01-05'), 'CLF5'),
(TS('2015-01-05'), 'CLH5'),
(TS('2015-01-06'), 'CLF5'),
(TS('2015-01-06'), 'CLH5')])
exp_prices = pd.Series(
[100, np.NaN, 101, 102, 103, 104, 103, 104, np.NaN, np.NaN], index=idx)
assert_series_equal(exp_prices, new_prices)
# standard subset
idx = pd.MultiIndex.from_tuples([(TS('2015-01-01'), 'CLF5'),
(TS('2015-01-01'), 'CHF5'),
(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-02'), 'CLH5'),
(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLH5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLH5')])
prices = pd.Series([100, 101, 102, 103, 104, 105, 106, 107], index=idx)
widx = pd.MultiIndex.from_tuples([(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLH5')])
new_prices = util.reindex(prices, widx, limit=0)
idx = pd.MultiIndex.from_tuples([(TS('2015-01-01'), 'CLF5'),
(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-02'), 'CLH5'),
(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLH5')])
exp_prices = pd.Series([100, 102, 103, 104, 105], index=idx)
assert_series_equal(exp_prices, new_prices)
# check unique index to avoid duplicates from pd.Series.reindex
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-04'), 'CLF5')])
prices = pd.Series([100.10, 101.13], index=idx)
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLF5')])
with pytest.raises(ValueError):
util.reindex(prices, widx, limit=0)
def test_calc_rets_missing_weight():
# see https://github.com/matthewgilbert/mapping/issues/8
# missing weight for return
idx = pd.MultiIndex.from_tuples([(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-04'), 'CLF5')])
rets = pd.Series([0.02, -0.03, 0.06], index=idx)
vals = [1, 1]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-04'), 'CLF5')])
weights = pd.DataFrame(vals, index=widx, columns=["CL1"])
with pytest.raises(ValueError):
util.calc_rets(rets, weights)
# extra instrument
idx = pd.MultiIndex.from_tuples([(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-04'), 'CLF5')])
weights1 = pd.DataFrame(1, index=idx, columns=["CL1"])
idx = pd.MultiIndex.from_tuples([
(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-02'), 'CLH5'),
(TS('2015-01-03'), 'CLH5'), # extra day for no weight instrument
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLH5')
])
rets = pd.Series([0.02, -0.03, 0.06, 0.05, 0.01], index=idx)
with pytest.raises(ValueError):
util.calc_rets(rets, weights1)
# leading / trailing returns
idx = pd.MultiIndex.from_tuples([(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-04'), 'CLF5')])
weights2 = pd.DataFrame(1, index=idx, columns=["CL1"])
idx = pd.MultiIndex.from_tuples([(TS('2015-01-01'), 'CLF5'),
(TS('2015-01-02'), 'CLF5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-05'), 'CLF5')])
rets = pd.Series([0.02, -0.03, 0.06, 0.05], index=idx)
with pytest.raises(ValueError):
util.calc_rets(rets, weights2)
def test_calc_rets_two_generics_nans_in_second_generic():
idx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5'),
(TS('2015-01-05'), 'CLG5'),
(TS('2015-01-05'), 'CLH5')])
rets = pd.Series([0.1, np.NaN, 0.05, 0.1, np.NaN, -0.5, 0.2], index=idx)
vals = [[1, 0], [0, 1], [0.5, 0], [0.5, 0.5], [0, 0.5], [1, 0], [0, 1]]
widx = pd.MultiIndex.from_tuples([(TS('2015-01-03'), 'CLF5'),
(TS('2015-01-03'), 'CLG5'),
(TS('2015-01-04'), 'CLF5'),
(TS('2015-01-04'), 'CLG5'),
(TS('2015-01-04'), 'CLH5'),
(TS('2015-01-05'), 'CLG5'),
(TS('2015-01-05'), 'CLH5')])
weights = pd.DataFrame(vals, index=widx, columns=['CL1', 'CL2'])
wrets = util.calc_rets(rets, weights)
wrets_exp = pd.DataFrame([[0.1, np.NaN], [0.075, np.NaN], [-0.5, 0.2]],
index=weights.index.levels[0],
columns=['CL1', 'CL2'])
assert_frame_equal(wrets, wrets_exp)
def dates():
return pd.Series([TS('2016-10-20'),
TS('2016-11-21'),
TS('2016-12-20')],
index=['CLX16', 'CLZ16', 'CLF17'])