def test_volatility(client, data, price):
d = json.loads(
post(client=client, data=data, url="/engine/volatility").data.decode())
x = parse(d)
pt.assert_series_equal(from_nav(price).ewm_volatility().dropna(),
x,
check_names=False)
def test_ytd(portfolio, navs):
#print(from_nav(portfolio.nav).ytd_series[pd.Timestamp("2015-03-31")])
xxx = ppa.ytd(frame=navs)
print(xxx)
assert xxx.loc["test"]["03"] == from_nav(
portfolio.nav).tail_year.resample("M")[pd.Timestamp("2015-03-31")]
assert set(xxx.index) == {"test"}
def test_from_nav():
t1 = pd.Timestamp("2010-10-21")
t2 = pd.Timestamp("2010-10-22")
t3 = pd.Timestamp("2010-10-23")
nav = pd.Series(index=[t1, t2, t3], data=[10.0, 10.5, 9.9])
x = from_nav(nav)
pdt.assert_series_equal(x.nav, nav)
def ewm_volatility(frame, **kwargs) -> pd.DataFrame:
d = {
name: __attempt(f=lambda x: from_nav(series).ewm_volatility(**kwargs),
argument=series)
for name, series in frame.items()
}
return pd.DataFrame(d).dropna(axis=1, how="all")
def recent(frame, n=15) -> pd.DataFrame:
d = {
name: __attempt(f=lambda x: from_nav(series).recent(n=n),
argument=series)
for name, series in frame.items()
}
return __last(pd.DataFrame(d).tail(n).transpose(),
datefmt="%b %d").dropna(axis=0, how="all")
def test_month(client, data, price):
d = post(client=client, data=data, url="/engine/month").data.decode()
x = pd.read_json(d, orient="table")
pt.assert_frame_equal(
x,
from_nav(price).monthlytable.applymap(
lambda x: "{0:.2f}%".format(float(100.0 * x)).replace("nan%", "")),
check_names=False)
def performance(frame, **kwargs) -> pd.DataFrame:
d = {
name: __attempt(f=lambda x: from_nav(x).summary_format(**kwargs),
argument=series)
for name, series in frame.items()
}
d = pd.DataFrame(d).dropna(axis=1, how="all")
print(d)
return d
def ytd(frame) -> pd.DataFrame:
"""
:param frame:
:return:
"""
d = {
name: __attempt(f=lambda x: from_nav(x).tail_year.resample(rule="M"),
argument=series)
for name, series in frame.items()
}
return __last(pd.DataFrame(d).transpose(), datefmt="%m").dropna(axis=0,
how="all")
def mtd(frame) -> pd.DataFrame:
"""
:param frame: A DataFrame of Nav curves
:return:
"""
d = {
name: __attempt(f=lambda x: from_nav(x).tail_month, argument=series)
for name, series in frame.items()
}
x = __last(pd.DataFrame(d).transpose(), datefmt="%b %d")
return x.dropna(axis=0, how="all")
def test_mtd(portfolio, navs):
xxx = ppa.mtd(frame=navs)
print(xxx)
assert xxx.loc["test"]["Apr 20"] == from_nav(
portfolio.nav).tail_month[pd.Timestamp("2015-04-20")]
assert set(xxx.index) == {"test"}
def test_ewm_volatility(portfolio, navs):
frame = ppa.ewm_volatility(navs)
pdt.assert_series_equal(frame["test"],
from_nav(portfolio.nav).ewm_volatility(),
check_names=False)
def test_periods_per_year(ts):
assert ts.periods_per_year == 261
x = pd.Series(index=[1], data=[5.0])
y = from_nav(nav=x)
assert y.periods_per_year == 256
def test_performance():
ts = read("ts.csv", parse_dates=True, squeeze=True, header=None, index_col=0)
pdt.assert_series_equal(performance(ts), from_nav(ts).summary())
def test_sortino_no_negative():
nav = pd.Series(index=[pd.Timestamp("2010-01-13"),pd.Timestamp("2010-01-15"),pd.Timestamp("2010-01-18")], data=[4.0, 5.0, 6.0])
assert not np.isfinite(from_nav(nav).sortino_ratio())
def ts():
ts = read("ts.csv", parse_dates=True, squeeze=True, header=None, index_col=0)
ts.name = None
ts.index.name = None
return from_nav(ts)
def test_recent(portfolio, navs):
x = ppa.recent(navs)
assert x.loc["test"]["Apr 20"] == from_nav(
portfolio.nav).recent(n=20)[pd.Timestamp("2015-04-20")]
assert set(x.index) == {"test"}
def drawdown(frame) -> pd.DataFrame:
d = {
name: __attempt(f=lambda x: from_nav(series).drawdown, argument=series)
for name, series in frame.items()
}
return pd.DataFrame(d).dropna(axis=1, how="all")
def __series():
return from_nav(parse(value=json.loads(request.data)))
def test_drawdown(client, data, price):
d = json.loads(
post(client=client, data=data, url="/engine/drawdown").data.decode())
x = parse(d)
pt.assert_series_equal(from_nav(price).drawdown, x, check_names=False)